{

  "nbformat": 4,

  "nbformat_minor": 0,

  "metadata": {

    "colab": {

      "name": "mura_bone_fracture.ipynb",

      "provenance": [],

      "collapsed_sections": [],

      "machine_shape": "hm"

    },

    "kernelspec": {

      "name": "python3",

      "display_name": "Python 3"

    },

    "accelerator": "GPU"

  },

  "cells": [

    {

      "cell_type": "code",

      "metadata": {

        "id": "mNLtiClOZEvZ",

        "colab_type": "code",

        "colab": {}

      },

      "source": [

        "# Get the dataset\n",

        "!wget -cq [redacted] # See README for MURA Dataset Research Use Agreement\n",

        "\n",

        "# Unzip\n",

        "!unzip -qq MURA-v1.1.zip"

      ],

      "execution_count": 0,

      "outputs": []

    },

    {

      "cell_type": "code",

      "metadata": {

        "id": "vB0hgX4RZ7EI",

        "colab_type": "code",

        "outputId": "3beb0d03-4d5c-4008-b3a7-36a31973f713",

        "colab": {

          "base_uri": "https://localhost:8080/",

          "height": 33

        }

      },

      "source": [

        "!ls"

      ],

      "execution_count": 0,

      "outputs": [

        {

          "output_type": "stream",

          "text": [

            "MURA-v1.1  MURA-v1.1.zip  sample_data\n"

          ],

          "name": "stdout"

        }

      ]

    },

    {

      "cell_type": "code",

      "metadata": {

        "id": "shsEIxUxZ-d6",

        "colab_type": "code",

        "colab": {}

      },

      "source": [

        "import numpy as np\n",

        "import pandas as pd\n",

        "import seaborn as sns\n",

        "import matplotlib.pyplot as plt\n",

        "from matplotlib.pyplot import imshow\n",

        "%matplotlib inline\n",

        "\n",

        "from keras.preprocessing.image import ImageDataGenerator, load_img\n",

        "from keras.applications.densenet import DenseNet169\n",

        "from keras.models import Model\n",

        "from keras import layers\n",

        "from keras.layers import Input, Dense\n",

        "from keras.optimizers import Adam\n",

        "from keras import backend as K\n",

        "from keras.callbacks import ModelCheckpoint"

      ],

      "execution_count": 0,

      "outputs": []

    },

    {

      "cell_type": "code",

      "metadata": {

        "id": "yKnyzvgQZbnF",

        "colab_type": "code",

        "colab": {}

      },

      "source": [

        "data_dir = 'MURA-v1.1'\n",

        "\n",

        "train_dir = data_dir + '/train' # Path to train directory\n",

        "val_dir = data_dir + '/valid' # Path to validation directory"

      ],

      "execution_count": 0,

      "outputs": []

    },

    {

      "cell_type": "code",

      "metadata": {

        "id": "BkzhyHCB09Z4",

        "colab_type": "code",

        "outputId": "be971542-ac23-4464-c4f3-5730f625319e",

        "colab": {

          "base_uri": "https://localhost:8080/",

          "height": 284

        }

      },

      "source": [

        "# Display abnormal bone image\n",

        "img_abnormal = load_img('MURA-v1.1/train/XR_ELBOW/patient00069/study1_positive/image2.png')\n",

        "print('ABNORMAL')\n",

        "plt.imshow(img_abnormal)\n",

        "plt.show()"

      ],

      "execution_count": 0,

      "outputs": [

        {

          "output_type": "stream",

          "text": [

            "ABNORMAL\n"

          ],

          "name": "stdout"

        },

        {

          "output_type": "display_data",

          "data": {

            "image/png": 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n3+pnGthbjP2xBPJW/6phtjVSC7Q55+YQMXlkdtulmWg6D9h5P6KnaVNKEw9ceYRBytyD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\nJePnqnQ6neYAqdOnT+MFL3gBbrzxRgDA6173Orzvfe9r8n3Tm96EV77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BgywsLPDUU0+p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\nAAAAAElFTkSuQmCC\n",

            "text/plain": [

              "<Figure size 432x288 with 1 Axes>"

            ]

          },

          "metadata": {

            "tags": []

          }

        }

      ]

    },

    {

      "cell_type": "code",

      "metadata": {

        "id": "d56h1_Qd09JU",

        "colab_type": "code",

        "outputId": "8914fdca-ff7a-4559-e0cc-3a6d7fca49e7",

        "colab": {

          "base_uri": "https://localhost:8080/",

          "height": 286

        }

      },

      "source": [

        "# Display normal bone image\n",

        "img_normal = load_img('MURA-v1.1/train/XR_ELBOW/patient00011/study1_negative/image1.png')\n",

        "print('NORMAL')\n",

        "plt.imshow(img_normal)\n",

        "plt.show()"

      ],

      "execution_count": 0,

      "outputs": [

        {

          "output_type": "stream",

          "text": [

            "NORMAL\n"

          ],

          "name": "stdout"

        },

        {

          "output_type": "display_data",

          "data": {

            "image/png": 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gLbEiux4xGk6ncZTVlOqu+rSYGzVfnJpTkVgZ3F58r/hBQGaZxoAY/PkBRnZW\nR4zNA2oDfW9FbQM+y5XBAJC34mOyCp+ODhLbb4dXqjzRvWm8DsxL7F64nbMuNebnmWAqLWHKkBQk\nPAbWMvg80NA8tb6cjn06utrEZpil293dnaUxM5fjeVvI8iTD5h/7eko+ra7gs2FLdRkMAC1LSv4j\nZjC2t44BSbTiU2Mdmq8HIgxqkTmiszEzAq9MT6cILCKzswWcmIFE9dd8vXKjdCy6lYYBhP0zWBwe\nHs6W4e3+2b7Y3tPRqpenk7cxmunVZxLcgE9dBgNAfWeYUpiZUQYADDberMwSLU1HZhSn8cwuBRo2\nKzwzDpj/+meJhXAcj5Hpv9bVC4vef/IAWhmPxuV4pXowQ5lMJqfaxOrJe/8w8+E9hcxv5IGHsh2t\npx6rs3sDLLF0BerBAJAnpZtdcloa8OjgtzheOvsv+T48YPH01HKjazWTxmZ3IH4KtwRIUZwurCQC\nLg9cakDoAR1ftzqa34aX3DmtvYRq99o2JmP/T22b1kiXKCx6F86LeyMDVNe6DwaASis6Jt5szKtS\nHt2OyvBmPguP2IsONM3XYxteXSKHqKblMGYYEYvwnK0qHjhoeV7enm5ePTX/lnyVcfD3zwyUjNGY\nE9rAOeeMm266aY4FjUajU6thKl2eatYvsXqTU6l+G4llMADkiTcb8eoUA04EEup41Lw9c8ziem9W\ntzCiaPbna15njvJm8dgT5+WVrc5UDee4ll+Lmabt4Z23MALOW00mu7a9vY3Dw8O5T+TY/d/d3cXV\nq1fn7tne3h6uXr166tterBPXMwJJM8FKk8tGrsl1YYJZx4ievfEGqvp++LjESKJB3rI0rmkU8Lx8\nPdNMTRevPRgcItDSurGuPDg1rce+PFHWF+VVMsk8tscgYKaV7XjoMQ+7p9vb27j55ptnL60yOBkT\nMjOOX27lMrWdLa2VZ6ad1ZnZ0AaEFpfBAJB1EqbWHhAAOHUtYhDKYrw4LXpFoKHlMuVvoekl9hCZ\nOAwmGo/TemVFTKmkY21G8+pQ8vNE5VlZR0dHs8chmH3wNhsWbuyGQcLyYX+Sx/xqddKvY3iMMJIN\nOLXLYABof38f+/v7ODw8xMHBwalvTNWofmQKtVBnBQ0FEpPaI/msK4OEXfM6pg6ICDAiaWFAUXhL\nGR6DKZWjAzZiaZ4vSk0/BiQ7Zx/QaDSabVJvy/L8mgbn4zFSrzytG9eHfVIlv1Y0kSxLhgpwXc0v\nYEAAxDsBRsCjTMNEWQ6Ha5gnXJb6hCLAqbEY/Vdg8xhOF5CNzBqN77VLC7CV6qT5RyDTCl6su008\nZjrxu3TmXB6NRtjf35+9ljFY42UAACAASURBVHH16tU51mKvb/CHHL2yIlGT16t7FNYnTlcZIvgA\nbexSZTAABFwzw/R5Gs8EiQaUgpg3ADzAisCOwyOzyAMG1s0zl6J81N/BZZfSt7IeLyzS20vXdQB7\n4ZaP5mWMx770asvtls4eMrT7d3R0NHNMc58Zj8dzIGQfNGxhoMrgvO/Ct8hZs5TIFG/RYRFdW1hx\nSQYDQN67V5HoQC3RfE5j5fB/KyBwGr1eAoESYJXq56VpYTqcrstsVHqB09OtlDcDjGeG1mQ8HmNv\nb2/O38MsyFa47HtgBk67u7unHNHstGY92JnsAaH98/WS2dVnENfSlK63DPyuDLePLAqygwIgEwaV\nqJE90NEZ3BiVV4aXn/3zTN0FLLSD6oxfYlKtZpGXl1d+FBbp31K2l67GmqJ00Yyd88lrFt5+SBaH\nvwumXwQxMd/QZDIBMH/vI7+PhqnPJ9o2RHX0zru2RS2Ps2RXq5TBAJCCDv8i6uyde7Nd5ISMACi6\nFnW6UpqaqaQ+lKisaPbtMgt6ZlaUxgPTSJ9SeCQaX9thPB5jd3d39o5XSifL7qPRaPbMD38Tns2y\n0Wjkbu7v1cEDadbJczpru3gg3IXZ9GFBXRnXWZuErTIoAIpMsIgJlcJL8bRc71+vcz7KYFqYRqmM\nUtzovI+UzCwPdIB4ZagEdi2sS80bE33q2N4L4201+F0wnljM5DJ2xL4jD0RVFAwtz9J7YIswH6/8\nUn4Rc+6SX6v0TdvF7AcGBEA8W5VehIxWqey4K631BqGCSgQ6UT41eu5JK4h5ZUYzeakcr36lMlo6\nvF3n+BxWEwWMlNLcN8D29/dx+fLlmZkGXPsihj1oqC/F6jthkU/Hq7PWJ2I8tbqW+oZOCKX2benP\n0TWPeZfS9wWurukGA0AmXAHvhdJSo5bYkBfease3sKNWPSJTKqLlpQ6tvgyvk0XllvwgJXDqwnyY\nhXjsonRu4JNSwu7uLsbj8WzFS/MDgMlkMqeHvZBqgGarZ+y09sSbdFoGlU5QrQyiS94teS6z3LOQ\nwQCQLZfa8ikDTwkIPBDx0N2bwb0VmtIrFCXzpUVqrGyRGa52vdRmqmMEXLWyWwdHCYSBa/eFndGj\n0QgHBwczvwzvjMjp9aupdl1fwzA9ND3vOaTXSuDp9Y9Wk8oDbgXA6D7ovSrlH92f0uTV1RTryuLL\n38I5Q0np2jNAdlxrfODaC4mWhzWYPrXMoBPNahHQeSCn4Kh58HmNSXW9aTWJdPLovw421pcHgNeW\nrSafJ56JDVxjvfx2u72IasyG9wLSAWZpeTsPy8ert10rmY6R2cYgFg1ULSvKpzSxeWzVA0iNW8rH\nu+6Fde2rXZnVYBgQg48O7BINZsDh+Jq2NBuVrrVscaHShSa3zE7edR140WzKeWjZNV292d27XitD\ndY6u6QOiGlcfRLQwYzzANfbEW+fqxm4lvfmYgakEUKyvtptXzygfb+LzAF/bKGKUWq7Hbmvjqsaw\nPH27yGAAiFmLvmlt1zU+CwORd0N0MPH1WqMter0mLSCgYR6IlWZBTrdsWl0CjZKeLHbf2Kls+dk1\nAxq+z+br0VUxHZwROKsw6PCuiqX9gxTQaxNMF5OoFVxa8q7p5E1MpckqYnxdZDAAZKJU30NXrzO1\nMiYtQ8Nrg7O1I2tcvdk6u0VpIwrsMSAup4Vud6lLJKX0tbpFezHpPkzsE+T0vG2GhfE7YKafZ+7V\nwlOa37C+9EpG1AZdgMgzqfT+ROZV6R54+bSw72giKY2PPhPxYHxAPCC9iqjvxo7tFY4I8T1g8sJZ\nBy8+x4vyiSRiXbWOY//209cHonp45bdIa12i89JA1POo7XQg2nYao9EIOefZy6kMTBbftmi1tmJz\njPNW8NFjDjMQatlBkXXxzKWojbpOAl3YlMXRyTxKF+nXAoal8EiqAJRSuiel9LGU0l+klP48pXTf\nNPyZKaUPpZS+OP2/fRqeUkq/klJ6OKX0uZTSS1oU0UfvvUHOoONdiz6pw2nsnPPTY6cNeqF7RJ9L\ns2GpvEUYiwesraypVH5p4vAYmoGHvnAMnPQB/sqFMZmjo6PZw4gMPru7u3M+IN44zNLyvtGtL5Yy\nyNd2VGgF4Jb71gIGHN4FiBR0uA8oWHI+annwsTdJdpUWBnQI4H/JOX87gJcB+KmU0rcDeAuAj+Sc\nXwDgI9NzAHglgBdMf/cC+PVmZZyVqxoL4TBNp9LCZrz0Nbs6ArJWRuKVF9Ht0iAv6aVldzWdanVh\nPQwAIl9eVLZNQnt7e7PJhFfAbIl9Z2cHu7u7AICbbrpplnZ7exu7u7tI6dp2HvZrMaFYLzXvamn7\nTFBRXovE57qUdPfAqNRfPInGUBep+oByzo8CeHR6/GRK6SEAdwF4NYDvnUZ7N4A/BPCvp+G/k09q\n+N9SSrellO6c5lMqZ47hePvwmtQGSEujWZmRTasMQWeQmn1fAyGWFh+CzkgtM16t82nc0kyo4Tln\nl3GmNP/gH4AZgPA91rrddNNNs/vO8XO+9g2wyWQy92wQ52PbuKZ08gKqmWr6uoaCu6VXEyt6HigS\nj+15YF8C4GWLV25UftSfu/azpZtgLCml5wN4MYBPAXgugcpXADx3enwXgC9TskemYZrXvSmlB1JK\nD0zPQyStIXTrdQ7zrvGxziBe3Che0HZz56WOXQJGLpPPVSfNywMXzbOkswc++jAgTyB2vrW1hdFo\nNHeub6/fcssts+03zJQyUPDeDbMnore3t2dvvdte0vYPYJaX6esBr4EUt1HpW2KlNvImptZ8+kqJ\ndXisU++jivalGovqwpg8aV4FSyndAuA/AfhXOedL0jlzSqlTy+ac3wHgHdO8c+vA9cJakNqL580G\npYGveWh5ETOyY4/R1PRT3fQ4qofGX0RYd8vTGIbq6cXlF0lt2dzC9/b2sLW1NXvVAoALPLzUbszL\nttoATkwxM7vM3LKn6r2+4bEg1t9zXHv3zLsnJaaxbOnCzkrXvAm6xIQjYOpa5yYASimNcAI+/zHn\n/L5p8FfT1LRKKd0J4LFp+AUA91Dyu6dhLeW4q138z3FL5y1h3FjeA4cebW25mUphFZgUaLSM0o2N\nQNP713q0isXnMm210ViPDXLVlU1nYz+2RYaZSPv7+3OvU5jZZUzG6pDzNYcz62H/BljGgmxDMn0p\nlf0/3EYMNHzd2/enq5TuF8uygKq1r7ZMVl5f9fqxF9ZVWlbBEoB3Ango5/xv6dIHALxuevw6AO+n\n8B9LJ/IyABdzxf8zLScEnlaa56F4q0SzeSu7Uj0ioFP9WoHNm5U4LwMI26x9d3cXe3t72Nvbw87O\nDvb29rC7u4vRaDR7544fX+A62r47/H7eaDQ69biDl1bfpeNPKxlIGLAA17bbMObCYGMrV3wfDFCM\n/bAJxU9DW1qv7/DSOre/mmIlBhpJyWTRdrM8l2GmtfQj7zqPO6/vcxxvgrb/rmPEpIUBfQ+AfwHg\nz1JKn52G/TyAfwPgvSmlNwD4EoB/Mr32XwC8CsDDAC4D+Jetynjg02paecddJWJAeuyVr4PE00sp\neqk+SvvVnDHAYcagD/BZHt6G/6xHyeTQBQF9KpjDdQLhTySbb4Y7szEkflHU0rLz2OqizmQrS/d8\nZnbD74yxbspyOB3fQ48llaQFTPge8T1TFuKZd4v070jfiJF711WXRfVqWQX7JIAo9+934mcAP9VH\nGe0k3kCNKqpxtWFaqa6Xjsv2QIHzVTCKKGyN8Wi9+DvxJswWdBZTIOJlZbvOjz2wk1bLV8CxuDxI\ndLMzY1HmLPYGvJlXDBS8xSqXz2BqzObw8HDmeOYlegM0fWfMYz4KwF0c0IsIP63t9bWSeGDghbWK\nByglEPL6dEu/9mQwr2LoO1zaEFq5GqPwaGMXSu3lWaO53kxbSl9jQsC1bUrYr8HvQ3F6Njm8TqOD\nS5+V4cGqeTDDUtbFdeIvWuzt7eEb3/jGHLBwPlx/BhZ1QJswczHwnUwms1U2jzWovmp21fqVB96q\nk6dnqW/pPfKk1je6gBbfm0gvj83UJtsWPWsyGADSikRUkCU6j9C4pVNwvNZO5NWjZSbwbhwzHn6W\nhZ+50e0oPKD1Xso15mJgxtf5tQZvImCzSevATl4r+2lPe9ocS+IvnSpwsh/IAwN+nkgZrbIr/kSP\nAjL/cx4WrtfUJ+SJd60LCyj1ce5LtXqU6qYsl9sx0rnWXlpuXxAazLtgv/3bvz03wFgUmKIBzp3F\nmwk5L89sUfHKteNSx/TCSzeI89rd3cXNN9885y/hVwssns36nL+ZGbYSxMeTyQTj8Xhuv2SvbbwX\nRLkM9dfYOZsyvGePlRG9CmOOZ/ZrqZmor0NYGK+Y2pK+nev+P8A8s/PuQXStiw+oRTxzhvWo9e3o\nmjeJ8nVl6JF4ZKCV1XeVwTAgwHcm639tligxJb3pXnxv9te0qqvGVT31Jmmn45UiftmS9WenrM1i\ndqwvZVq4DnxPNy1Dy1Z/SeQQtrj7+/uzF0cNBI3RcXtzvjs7OxiPx3P6mn9H9bd2sK+iMtCwP0lZ\nED+UGIkHNN7gXkR40lNdurL2WtroGk8OkZTGRUnXru00SADSGbo0gHhW1Ly8gc/n3vWa+cTAxXlq\nmSXw0Rlcn33STbXsuKUuHk3mfEyiZ12MxSgzYoZj8eycXw7d2trC7u4uDg4OZnXkbVINMHlWZscx\nh6mpafkZODEg6jNFBoJRnbmsEuPoO7N3lVo5OgF7/cAz17y4UZ/V8jzQYX1L/b1VBgVA3sBRJqDx\nLFzTltJEDVe7qbXBzrNlBDjRm/ycHy9p81vc0WzMzmHOS/089mKnlldjguyEZRpvA//4+Hhm/tiq\nlIk+ROi13Xg8nntQUfVjHfiNd9PTQO/y5ctz+wGpGebdS62jBzzLYj+LSG1StH/vHgG+j9PEGyMR\nKGtedt6XJQ4KgIDY3tSGZWmlrbVOqJ2V8/LK8MpRX4aCjdZRWY7ly9tLeDqyntELl9pOzDL0+RP7\nqVnDeVr92JThb7gbCJlfx9rAwHF7exuTyWSO4fH7ZKPRaPZUs7YH9wm+h9beBwcHc++BWR2sTANL\nftgxYsIspRWwruIBg0mNeXv5eH2wNknytb4StVuffAcFQOqo9BhFSyVLIFNqNG/W9/I2X40CjNdJ\nIx24fPOVlHTjGb9kMrBJxMKmDIBT16N8eUmc38cy2d/fnw3unDMODg7mVvAs7WQymW2VYU5nAx9m\nZuxctzJzznPMyoDS7oM9GW1fROX6Gqtilqj9qnR/WvcQapVaf+ybD9DfDIqkZFloeN8yBwVAQNnn\n04XmRfZqjT1xfAWbaCVHgYIdnuqbsvz5pyaOHZdos9ZT84vMHW8A6IN/KaW51S0Ac3kaeFhdDQQs\nnI/NROOHBXlVS9mb+Yf0VRF+zYLLvnr16lyetqRvOjM4Rt8U0/bz7uuissy8WEr3tmufr4Vx23D+\nEUi1yOAACJh/alUpdwmgvPNWxsMzqgKNd2zpuSxzgPLSsQGCmhUmBmwWV58o1nrqAGHwMr01nZoR\nGkfL8wCR9eL9eAysRqPRHAgcHx+f+lgggLkHKs0EM9NpPB7PdNL3wowBGZAYYzKWY+aftTW/Bc+s\nkOumfh/Wc9mAEQHaokDnDX6PnSzDhNQyW4GsJIMCIAacSHSmLs3sXjgw76exWdZjNwqACiaWl3Zk\n7vzeoI709tiV/XugqmZqtHm7tonmYYNR2ZCnH++5Y2zHQJu3x9ja2pqBErej6Qlc2y4DwOzVDWtj\n1ocZFIOulWMvuDI42bn3wi3fDwYfflWDz5cp3LYl8CmZWCXhceFNMn2kBpLK3LvIoABIV7xqs1IN\ngW3AeKtQXJ6CjMdWNI3FnUwmc34SHmAKNiWJWJ6yHM5Tl9J1Od8zD1l3rRfnyYDLJhFwDTjYJ2TH\nzEwsjsXnr5Va25kOutJnW2tY2+qjAfzsVM557mVUbXfbDsQmGh2Y2h7cF5bFHroAWTQ51XTRdNqn\nOF5totd8a2DpgV6LDAqAarN1JNaYvPrkPWdjcTkNd26+Dsyv+GgcbWx9j4l1j25gZGaVHj9gsOMV\nJO2crKv6eLSedm4mEdfXTC7+aICZURbXnMj2xDWboLaVh+1iGD2vxeaXbTZvPiPT2xiV95gCAwc/\nzGhlqE+L20L1UQa8DIkmmEjsnt5+++24ePHiqUcsovyjvJR5ddW7Ngb7MsVBAZA2jDcz8XYUDCLe\nOdN/4PTDdwwqJX8Ah5c6rQKNAhbnZYOHWYmJ+oEY2DzQMdBlcNKyStRfQdIGtG0OD1wDHXZG60Zf\nNvj5uppVAOZATtvLylbQs+V1nhSYpTHr5Py0LUuLAl7YMhlQCUC8+E888cTs/I477pgzcb34LWGq\nS4serdLK9lkGBUDAtU+q8OBjU8ljNpH/hjsog4+yHg9coieF1VTTTqpxPZ0iphINAq57yYzwAAvA\nnHlT8mswoFk62wSeWRczDjUHueyjoyMcHBzMNkJjc1UBn++xMSK+3+pUNuH3yHT1kdtUzVjtD9oX\nrM8siwFpO1u53jUPEPs8DuCxLQ98aqDRCix9WNCgAMh28Ms5z1FmZT8mCjI8EKwx+LF/TqdUW/P0\nJLoRnjljeelyuK54edTcmy15oEcMjZmBsQL1nXB81Zkf4mMzin091p78wqk+a8PfaveevB6Px9jb\n25uBDE8IbELbveQXYHWFjn1Qls570JDvnQ5IbcdVCfe5rsCmuzt6fVfL0n7SF0xLbeIx/y4yKACy\nQcDflOKObcvcwPyDdTzL8eDVWc7CPEaifhe7Ht00nrHtn3Vh4NGVJQVR79gkMg15luZVI23PqFPw\nkrkuq9sxg4L5aIyhmm78xDODvfpp2HeTc54xI347Xxkl3yfLn53OXEdmXtz2DIjM5Epsc1kDtySl\nwRr1A8+XV8rDA4dlA+yi+Q4KgMzu5y9d6iwGXAMRBhgTNT+8BtLnYuzcOniJHSmYaOdkH41ngll5\n3g3TQaCMRn0fWr5HuTncjm2Qel+jVR+L6WIOYHvqmFezgNO+GXZS877OPElcuXJlZm7rvbQVNW0z\nnVTsnnkTi8VhILX7rGCussgSfDT4WW+Lx2F87vmquF9qeGmyjMqspWmVvuwHGBgAHR8fYzwe4+Dg\nYM5fAJym09FqE4sOzghYTNTssIHEeSh4aV46SCwvzpP/1deks34kkR0fdXh+0NLSMuByWgMX3hDN\n/EFWD9721Mq3QW7HvOOifqPLzK/xeDxbqjcz29Lae1xaL95iw86V3bAJFz1uwW1nP97So+9rGNqe\nHB71Pz23L76qeP0i6ivRJMf6LIMRLZLHoACIZztlQNGsrsuTTN9tIPEgURCJHlRTJ6YCi/ewobIV\nj7Gw/iolZtViDujMz2yOTSc2cyyugRFv8G579Gj+OsNz29t7bfZOF4OFrn5ZO3P76mqb5whmU44Z\nGK+g8XNB7LxnXfXecft6E8mi0mXw33rrrW54xKrtOAKiVZiRy5DBAZAujZcGakrXHKdeHN6ukzug\ngguXb/+lYzYrGIhK0qcD6ABnHT2dGVA5HbcTMwQWZh7eAAdO+9hyvvYwoTEsS8sMycDNGIU+fsBm\nlD1pzV/U4PpbfXh/IdYNuAZ6xuA4f+1jERvymG4fiYCm1mde//rXz44/8YlPFIGlBEKR/qsEJDbt\nazIoAFIzKfr0LzD/rIw657hxuTF0MDEbYtbC59phz0K0Q3nXuE2810n4mn7xgp/VYUbDZqeCH5tC\nutTNuvGCAZtBwLXP57CpxyzRvj1mpjgwP1lY2ez85nB7WpvvpfaNg4ODkP3w/7Leguf71NKHrC12\nd3dnYQ899NAsD83XA6GofM+KWOajBn1MukEBEDDvDGVzCjjtmONBEj285810unoU7b1j+kTUuU+D\nl+K3zJTWNvpIgveEMe8qyGCu72wZY+HlbH2cwfK0T+14PjhmTty5GWgY/ACcYmy2nYcBCbexOqLN\nT8SMicv1fIi8pO8BveXPb873Za/WZtEkUpK9vb3Z8eXLl5vKisqvlbssE63PJD0oALLvOpm5wG9O\n68DXAeCtbNhM5vmUVDwGpR1QO5XOnKqnit7o0g2zgWtgo+wwylN9P1aGLXezKaomJJtmDOwM5swM\n2CTj8iw+syzLj0Fua2trtk8Qr5SZPrwHEAOi6cTvlzHzYT1MZw3z7o39GKT6DkztI9p/rcxIfuiH\nfmh2/MUvfnEufk2n1nitcVYpgwIg7sil2UxXldhujxiNdVw1yXhQR7OG13m5g3mgwHH432NUXHdv\n+TVakuU81aTyHOGaXpe02YejppTWiR3Fdq7bZ9hgZjOKmY/tI831tzrt7OzgypUrcyYUv29n7cLA\n5E1MXK5+SdZjJrwNyKJSAjtPeCK45557ZuEf/vCHT+nr9dWoH5Z0WbcMCoCY/vJsywDBrMYbXNaR\neVmWB7nlo+fR4Od89T9iIx4r8fKL4ngMzGNjbFapWRnpr3X1nMM2CBmIGASijwjyBGGgZCab6ahs\nznYy1BVL29ReTUHTyfK0dOwT4vtgdeKntnUy0dXN6P52FQ/0u8qjjz6Kxx9/fFYXD1xrfc2T0gR6\nljIoADLhzqN2P8dp6SSlwe/NgF68Vp0VpJQ51HTy8lIzhn0d/BwMD15mL5afgpf5T7wXSi0PC7Ny\nGai4rrrCxI82WFzT2/Z95ryNCdmzX+yDsQcSrQzef0jBmScuK091020/TKzc0gufXWUZrOP3f//3\n8eCDD2JnZwef/OQn8bWvfQ2Hh4cYj8c4OjrCb/7mb+LKlSt48skn58r1pIsJeFYyKAA6OjqaPYho\nD6iZ6EzcBblrzERNLp0ZS4DRSoG9cjyd9JkVNic9E8vyNTaig4pB0DNdVWfe+pT1VibC7MNWtrg8\nfgjRzDEAs+dzzIluA/7g4GAuf0vLW3joxxh5L2lmYwyWlh+zMGZ9FkeZ47JZQZdBz2XfcccdM3Ps\nh3/4h0/F/dmf/dnZ8W233VbUW024dbMfYGAABPjMpmWwmyjK96Wn2mG040TgxMyFmZyJhbMpokCS\n8+mHAE0Hjc+ApHrydc2DfwxcbHbxYOV0lo/n+GcQ4HBuJ94+hJ+K1nZIKc02mmf/FJ+b2aVPbZve\ntroHYA4oWTdL15X9eOzWm1hY566siJ3Ri8gyzMFVyKAAyGZFz5zhG8c32EPx2jmHK41n0UHn5ecB\nHTD//Sp+DseE325Wv4Wl57ConiUdvXRcpucrMmBh84d1ZB8SMzW9T1wPZkYGDmwCGkvi5X1+JYbb\nxfJms9Pi8sOPXI+UEp566qlTj2Xwyt7Vq1c7M4JWZqztW8tza2sLt956K1JKeNrTnoY3velNuPXW\nW/HGN74xTPepT32qmm9U9jqZUBqCHZhSygDwoz/6o3j88cfx1FNPYTwen9qSo5JHa1lznbDWYfga\ng0pkbrFpwmlUlDFoOOsadfQaO2M9NE/19Wi+Wh/Vj9sgpTS37YaCFX81g80/fRnW0hqDYoAwc039\nS/xcEQPO8fHxTKcrV67MnM/cRpbG/CktfcETvUfR5OitwPK55qn3LmIx7MAvTVL84ceU0qlJcFkg\n9NRTT+Hw8LAps0ExIK/xPHbCHcVjRsD8g3EATq2QcD5qpnDeJnad81HTxkT3INIyvfDoHDi9rYiC\nFKeN8tO2ZSczXzd2wm3PwltiqDOc20gHhzqWuV62X7Plp/fXWw3LOX5hlR/FsK1iTRgAzexSp3pX\nKaVRZhvF8c5LOnnMtiVeVOa6GNDgAMgeRmQ6roOBbxB3Hm5snaG9Bm+50VqeZzpx2QoEuiKlQKoM\nSRkR6+qtBDLbMeFHEKxMNYG8tmGd2QRkvXVVju8RO7kZOPhTRVYPXqViQOfVLr6mwKf/bKrZz3uZ\nFsAcOJWYTwsr8NhOdP80jHWyY74XpXQtjIvLbgHJdcigAIg7KQ8adULyQAH8BrQwfmDOE77R3IH5\n5nszpAKFvgpiaXlLU9OL/zk/1l3rwr4kr5NqR1ZHNZuz3I7cPvz8jpkLDEQaX9uEgchAw/LjJ7pz\nvvZQILMocxxbOfw8EjD/IKJd42fCVNTk4l8NfKKwKI43CXYRu4e2D/Szn/3sObN2f38fV69eddNx\nuTVWbbryhLgu8AEGBkDWgPzgGAMOv3wIzIOM1wEYTCKAUlON6TnrxfopiACnAYTTqImi1NnqpoOI\n66ftZNc9s8VEmYyyMvvx1y24bHbamm7ec0Octw1wDuc9iLhcq7f3uoetYNmxTgzeRMF66b1ktsQm\nm7ZZX1Ed9FrJDCr1p5wzPvGJT+CFL3whbr311lN9/fj4GBcvXsRrXvMafOQjHymWA5xmXi0MaZUy\nKABiiQYerygpcKiz1NLYuWdSsCjoRA5iBRA1JUpgqHpp/lqWOrRLHcwzQRQAzOlq7aurWcxcFHi5\njpEoM1L2qW2Qc8bly5cxmUxcsGGAUbCJ2sLqoP4e7yXaPiZWFIfPS/l4cbRdWfcXvvCFbv150njb\n296GD3/4w3N18tqQJQo/SxkcAPFndzx2wo/KK0B5AGJiDa0+HL3OA57z5E7IzIAlmmk4XJeXWbzZ\nif9572aP/XCH29ramvucsdF5+wKpzqJsDvGPgbC0YqZ6cDg/h3N0dDQDGwVlbqdohYzryWnVtNKX\nZtVM1PsWgUsLS2q5FjG1CJA8k/K5z30uvvKVr8y1x9ve9jYAwHd8x3ecAjlv8tU+v07zCxjYMvzr\nX/96XLx4EU899RQODg7cB+CA0x1QbVoPQDzxZjdvIEVlcv5RR+I8mWVoPH3S28vDe0rZrpXq49WX\nZ1jNR/WMTDnt2OpnYSDwGJVncnrCQMX52oRUeoFU2U7EUJctytp4UuU6veIVr8Bf//Vf46GHHkLO\nefZKxdOf/nQAwCtf+Urs7u7izW9+Mx588EHcd999c5PYpUuXZmU+4xnPmPM3ct100lxl3Ze6DJ9S\n2gfwcQB70/i/l3N+fSyxBgAAGd5JREFUa0rpWwDcD+BZAB4E8C9yzuOU0h6A3wHwPwL4GoB/mnP+\nmxZleFDzIPHiRIgeiQ40njn536PSHmPyHNMeWNaYEMfjh/p0hgeuvYpQqqcH1t6xZ04pOJnwt9YZ\nCBh0vJ0hWRcF4GhSAebfy7JldNXJWxGM2sMD2VKaPoMzuicey7W429vbuP/++wFcAxyV9773vbjl\nllvwnOc8B29/+9tx3333zdK/4hWvAHACPJcuXZoD2JL+OqmuU1pMsAMA35dzfiqlNALwyZTSfwXw\nJgBvzznfn1L6DQBvAPDr0//Hc87fllJ6LYBfAvBPWxXimdnrONzIOpuZeGaB19DMnHjwe0wjYklq\nBmk61ln15DpzXl59WC9tgyhfLx/v345LIGP6lcph4NT6a/tbXdlsMtNMN0dTiQZYBGhdB1nfQan3\nTk3Yknhxbr75ZgAn+wHdeeedc3kCJ+D0t3/7t7N+8YxnPGPGoBRcWA9v8vbS9JXSBKlSBaB8kttT\n09PR9JcBfB+AfzYNfzeAX8QJAL16egwAvwfgf08ppdyglc2SStGtYfRJWk4XDVyOY+dchvcqgW5h\nGumas/999sgcaGFo6rfyOgnrxqCg1yw/BRaub0mUwjPAaOfWV2hYP2NI4/F4xmpadOJ703VwnPXM\nHrHekh5HR0f45m/+ZnzpS1/CU089NQu//fbbkVLCV7/6Vfzqr/4qfv7nfx4pJVy6dAlvfetb8Yu/\n+Iv45V/+ZQDA+9//fvzar/0agJM357/7u7+7qJuJ1xe7tFmpbl3yaXJCp5S2cWJmfRuA/wDgLwE8\nkXO2R0wfAXDX9PguAF+eKnmYUrqIEzPt7xrKcQe+rqwoG9L0OhtrfO7wnhnnDQoNi/wXHhho3lE8\nj314A1Xbp0W6sAFv5uR2UFOKz3XJ25gN727Ak4vHZmrshstet0R6eCzQi3/x4kXceuutc18Msfg/\n8RM/gfvvv3/Wvg899BA++9nPIqWEH//xH8dv/dZv4Rd+4ReQc8bHP/5xvPOd7yy2Cff1RdtuWW3f\nBEA55yMAL0op3QbgPwP4e4sWnFK6F8C9EjbbnIofutOVKYsbgYDHoDhOzYQpDXRlFnpNByGHeVIz\naaK6cTt4YKl19MqPfAYRu4wGWs7Xlu6t3uPxeAY6Xht7jKGLLBt8+gJaDXy8++uBgAfI5h8yAHr5\ny18+i/PMZz5z7hGH973vfXP7SEeArfdC+0kftrmIdFqGzzk/kVL6GICXA7gtpbQzZUF3A7gwjXYB\nwD0AHkkp7QC4FSfOaM3rHQDeAVxbBeMXEG32nMadNYr3bI79M8OJgMNjGx5QLMo4tGxlZ5ynx8Ki\nMr1B7DEIBsrIZwLEzmgvX2VmfK/MWazOaO9eLNq5VzFAluX7iFh1qdyug14nDsv/Pe95z5x7odQv\n7Lz0fxbSsgr2HACTKfjcBOAHcOJY/hiA1+BkJex1AN4/TfKB6fn/O73+0dxh9Npsah3aAww7t39d\nydLryxAFPW9w6nUPYBR8uE4tHUCZA6ePnLalPLw8DeiZ1aj5ZNd1uw6TaJVqGfdjVebYovlEdexz\nTzjcY0w1BhkxWs133WZsCwO6E8C704kfaAvAe3POH0wp/QWA+1NK/xuAzwB45zT+OwH8nymlhwF8\nHcBrW5U5PDzE5cuXZz8GoK7iDWYFBpWIgirL4rCo7BojUz2941IZkVkVPU7A7aH56lPDHsOJmCGH\ntbCdVXT4ZeVZyqcPQyldq91fLrdV165jRU32dUjLKtjnALzYCf8rAN/lhF8FcHrvyAbhb13V6GOr\n8OwRgRKXEaVVXbx4UZyIZkc3PWJ6agJ5rKvksymBi2eO1sSLu+4ZdVXSwoBbJqeImXv9uzZZapxS\nf/LMQu0n0QS8ShnUqxg2ePSFOxZlI9FNKrGYLvp4eWkZpXgKPt4MaGZnznluidquReVr/UpL8tr5\nPJDTNo/uQel5oOtVvAHLUmKvLYO5D3vxQGiRMgbpAzpLsZcmeXCo6VCa3VvYheZTm2FMeNBFDEoH\nNdfF/CrKYhQ0IqDzTC4vXak+NcZSAvuIFS3TtzNkaR2U3sTYt22i9uY+VQKh0r2pMbqzkkEBkH6m\nVyVq5FYWFN0Qz1fDoKH5e0xDB7Vn0mhcr5N6jw6o/p5onhHgROVqPhHgePmcd7NrGXWosUbrS4uK\nTnh8rKCiIMW6lM7PUgYFQNENKvlQosHsOU1LA5+lxGo03BvopcGvedY6QwmMNb9W00j1ikDKKzNi\npudZasDeMkBrfYo/L1Qqx/tvlS7MtmZBdClzEQAbFADZxuMHBwe4evXq7N0g7uz6egZLjYKWwMYD\nmghwvNdBatKVLdR0qKXT45Je3nmka9Tm1wMQeVK7X9xOJVDmlUSLEzmBNa9FpPWe1O57LV1fGRwA\nmZQGemQmqPlWoquRyaVhukLkOZSt7JJEpqXXUSPWVcvfmwEjn1fNB9DasW4UHxDQvZ1qLKQ0IXo+\nTw+USuUr6JXStPbjZcugAMieqLWXFvk73kCdKnsmkBevlN7bVsJL23Xmj0CzC9BEs5QCj2cmaR5e\nviXdWwD2egehLqaaB/BdGalnZi9DPH1KDKgrOHWyDIbQadL0VQwA+MEf/EF85StfwZNPPomDg4NT\ntJWlBAKeOVUKE32KYS3gE4HMMvwNy5JllTeEPjQEKbVnH5/OMqWmV1cppXvyySdxdHTUlOmgGBBQ\np5QmJd+Id855twAB66OmV99O1MIizlKWVd6NZIaVpDTINd5ZAlKtD/cBodJY6pLX4ADIttc0E8x7\nuZGPW1co9LyP6aTlb2QjLcK+xchh3WJSe2ERuHgmVqs7w3RoHVuLMOnBAZDtHWNvVkcrB9qY2vgq\nNbu3i3SduYbuH1kGDR9y/VYtHoDUHNAspTaMFg30OAIh1anvpL2suCqDAyD+VAxL6bwF2UvXa6tT\niw6yoQ/Ovh3oRgcf63ct7MLrvx4oRH7OaGHBmwQ8n2NJt65O5mVKeaPaNYjdUGY7LWkWkWgAeSAX\n3ch13LxVSleGd73Vv0VKK1b8X1oRi3wyfC0qg9O2LqZ4vlKLW2Jg0fmiMkgGpLLqzl3KvzTjWLh2\ngKGbXC1yIwLKsqSFCSlrqbGmGttUv1BNt1q8KM6y+8VgGVBr3D5SAwfPTvZW1by4N/LAPU91P4sJ\nQldjvfIjkys6ZwDzmJRnjrX0d0+XSIdlyuAAKNoPiGVR30NtoLT4kSIQKt3w1hWI8yalgTZUOYt7\nELkSPAcxg4DHjiKTrdZXS3E8XfRaLf2iMjgAamFA6+zotRtbu9n2fz2B0PXqA1p2P9MB7YEOi2f2\ne74mZUN8vebbqTmlVz3WBgdALLUbs0rxGI7OVIvo4q2yXY+D+DzLsk38yJ8SOaA5nufYbvXPRIx9\nUX/SMmRwAMTftq7Zz6sUj+noTS/Z55qHd25hpVWT8yDnzfxatdT8OhYWrVwp4/HONY8IuDRf73rr\nCloUdxEZHABFg33d4i1fcudgP0hkYkXLslG88yJDuk/LlGXdh8gHpOASxS/p5AEUx/Oc1F1Mq6i8\nZcngAMh7CDFiGmchHuUtsSPvBteckF6cjSxfWgZO6b51yYfj1lZNSytfXvwIdDxp6Vdd0y/TRztI\nABqSdAGGyMmssw/H5TI2oLRa6TIYS4OoxcTiuKWFi6ivRNJqptcmwVUu9HTpt8Ma7fAHYwsjOivH\ndKkDeHY6n5d8Wt6182aKnQdpbdMu/aklblfHtKWpXe+iVxdfneeHXUV/HCQARfZsJGflBK0BXQRQ\nXliXVYzzIOdF93XpuYpyu7Lz1olaJ88WU6+vDA6A9JtgLVRzneLdLHVGR2wueo5j3XXqIxu21i6t\nvqhoIaPGSFpXtVpZWUuavjI4ALJvg9WcvibrHLRRJ1H9Lcw75rzsWldqvZHzJYs4hltYTM1lsciY\n6WICtsggAUilhOitZtq6B230nIYCjrfqsaj0zWPZfpCNLFdazalVmfrLyGdwANTqG+mySlGL31dK\nbKXl0YHSg2G63BrNPCW2uKh0XW7uIxvgWo1ELEcnuyhddH3ZMjgAGo1GxQHXslJ0Fg1Xo7EMTp7O\ntfTe8yOaP8cr+QXOykHfJ97Gd7QaaXFVlFZy7XjV92dw+wG1PJ+wilmT87Vjbyld/1vy5f/oemuc\nWtyhy3nVe+hSGxetY6avC6DvuBwcAwJ89sCrSiYtz89oeo8xRMdenqt4JqLG8FoYoKd7VN8oXaRP\ndN7SBtebibUqAF0k32VOyl0Ap086lcExoJ2dnWqDKp30TBplNCYl02gV0rVzqM4lEy06jthZCUS7\nmraRU70ln/MsqwLURfJdNciv0pE9OAZky/CelPwHkQNX03lO3VVK65Kr55DWcG/W0Z9Xds1J3dLe\nNUBU/Za9kreR4fv3+sjgAKgGEDwQlM2UGFGUz6qka/6R491bqrfw1vJanNQeeEUA7pUfxavpdqNK\nnzbpMnksS1btlxwcAHnPAbF4JkpptvXincVMvWheHiB4IKWAwE5yDa/lFQFNCYhYSh2wK+u83lnT\nEOvn3b9Wa6QUtySDA6DSdhyRycXxvMFXathVzNRn4ags+cFUlEGpY5pZVg0o+na8kn5R/HXIWZY7\nNGZYGi+rksEB0Gg0moGQ96s5dUtmgPqHhuhQBOKVNo/F1Ew1rbem5zhaPh+zP6rEMksyxFlf5Sx1\nXFY/WbacpdticADk7Qeks7U3yGqO5pJT96wcpq03sOQw1vwi/1AEKpHZVXNUc5wufoEu1zfSLpHJ\n7MUrnXeRVYyNQQJQiwO6NPNHA9BbZeJ89TiSvjeiS7pWPdT5roDi1VP9RXZNTTMtx2NALcDVRc4D\nSxqCeH0+ilc6Z1nHBNEMQCml7ZTSZ1JKH5yef0tK6VMppYdTSr+bUtqdhu9Nzx+eXn9+J4XI/KKy\nZ/88CGqOUG9wqNmhg7Tlhp3FjWodiC36lfw/UX4RQHtllVbYusqGJbXLssFkHeDfhQHdB+AhOv8l\nAG/POX8bgMcBvGEa/gYAj0/D3z6N1ywlJ6iaVsqE+F/TRGaXDshlUtYWWcRsUb2ZESlj0XYrAXh0\nzWNSXWbYocv1BH4t5rt33BKfwxadfJoAKKV0N4AfAvBb0/ME4PsA/N40yrsB/OPp8aun55he//7U\noVeW/DE6AEqOZC9txIbUXBmSI7IrGPO/+ggic43L0Xy0TD2P4pxHIFqmzn39ZH3z7SqlcVaLz2GL\nmt+tDOjfAXgzgOPp+bMAPJFzPpyePwLgrunxXQC+DADT6xen8eckpXRvSumBlNIDHM4bkqlEq0It\nLELROvIRqcN7qBJ1CL1WYj1ReyqzrHVWr9waM73epXViZOnqf1ym6bsuqQJQSukfAngs5/zgMgvO\nOb8j5/zSnPNLpbzqlzE8U4D/Na7HdPjcM1M4/3VKzQSz/xZ2Apx2XEflROBeYp0ln9w62/K8gF9X\nPaN7eZ6k5WXU7wHwj1JKrwKwD+AZAP49gNtSSjtTlnM3gAvT+BcA3APgkZTSDoBbAXytVaEamNQ8\n/2o+1EyLKP5QxNPNM79MPPOLhTus5zPicjlNSS8PvFjn1pl9VSC17HxXPei75D+0/tpVqgwo5/xz\nOee7c87PB/BaAB/NOf9zAB8D8JpptNcBeP/0+APTc0yvfzR36AFbW1unGJAOjNIAU3Zj6dVvVPIv\nteR/ltJiBum1Vgdz5AcrtYP6k1Snrg7OLvGGIKvuA8sG4yG37SLPAf1rAG9KKT2MEx/PO6fh7wTw\nrGn4mwC8pUumvCNiDWxaxQMlIB6QkfliaYYqJZ2V+Wj8iBVquj7A3QLm530mX7Yssz1WyS496VJW\np/2Acs5/COAPp8d/BeC7nDhXAfxwl3xVPP9Eyw3ReNG5txoUmWJDBhwTb/BrPbW+Gie6rmEl06or\naPdhSjeSRP2+jwl4lv6iLmUM7klooGxy1FZxeCVL89CBpeaGDrSh+oVUovaJgEQZHrcZi4KNB0Se\nDpF/Koq/EV+W0XY1n+m6ZXAAZC+jAuUlXz3mweKZWp7DVAeYZ2p4rGjIEs2YfM0Di5LPjFlSy6wc\ntben040k66hzqT8MQQYHQMDpZ1ZKnbkVHFpnZGVQ591MiFiLtm/Nsczpl20a9JXzMCGwRPqedb8a\nUrsNFoC8/1LclvxYSuaWN9u33rSzurktnVaBO3K423HkH4scz4vKos7RIU4IfXQaEiCctQwOgDwT\niq+p9O2E3iCLVoK6DJSzGhStwBs5pzUfz0xTlsRhfUyriFG13usu11tl2YN/iGAyRKA2GRwA8XNA\nLZR1mTfcc9ZqeedNvDpEjIZNsZKDv5aPd011qIV75UfO9kXkvN7b1hVIoP5QaS3vVcrgAKjEgDjO\nMiVaASrpsc6ZrmXAmngmZAloOJ3HCNVZrc76yN+0aHuVdL1epEv9+pjFrT66s+zbgwOgnPPck9B9\nG8NbavbiqM/HylzUP7FK8UyZPs5fXe3zVv44fmS6eaaapvPMrWUA0/Ukq+5zQ2zrQQKQzrJ9xAOV\n2qCy8rxl5JIfZd3iAVLfFcOSGeU57Tlf77x2bUjtOAQp3ZtVtNW6239wAKRbsvZB7chPEQGHB0ya\n9jwtx3dx4pso6Hommx175ehxl7I30iaraMN135fBARBQ3hOoRVpn+NLzLPx/3qXF+euZVB4QWVyP\nXUbtWWKMfXwZ65Zl9ouIgS8rv0XjrVoGB0A//dM/XdyYvovUGjnya5ylA3wZ0gqYal6W/DN83fMP\nKVDxNU7fqnt0PkRZxcorny+Sf2vaofTjwQEQUP86aiSRKVWKVzNXSg7fodzEmj4KJCVHsDqU+b/G\nZKJ0rUvEN7K0ADG3Z6ufLcprGTp2ve5Jv5G+Ytne3u41E3jmVcmJ7IEL/9cc10ObraM6sckUtYnG\ntfPSNW8QdHXQD60NVVpXGFsc/KX8S64BL45nMpf6ZtTOly5d6qTvsmWQALSMZXhOX/OBRKtl3BEi\nJ+yQBpB2SpaIuUUdWPNt9U0MqT2igcznQHkwe2BcK7OPnibrBoSzlkECkDEgT2ozks74QLwq44FK\nbSbRss5aWplGqU5R3BZndYt+fSeNZbSn1tPzSbWaOyklXLx4cWGdNhLLYAEIqC/n6mDs0sE0z5JN\n3SqlGbWl3D5l9Y2/CFCUZJE8jfnWdHviiSd6l7GRYcmgAYi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            "text/plain": [

              "<Figure size 432x288 with 1 Axes>"

            ]

          },

          "metadata": {

            "tags": []

          }

        }

      ]

    },

    {

      "cell_type": "code",

      "metadata": {

        "id": "GIhMSs3g9zWI",

        "colab_type": "code",

        "colab": {}

      },

      "source": [

        "# Train set studies with labels\n",

        "df=pd.read_csv('MURA-v1.1/train_labeled_studies.csv', names=['Train_Image','Train_Label'])"

      ],

      "execution_count": 0,

      "outputs": []

    },

    {

      "cell_type": "code",

      "metadata": {

        "id": "fFMUdqY598QQ",

        "colab_type": "code",

        "outputId": "0607a168-6609-4751-e9ae-153d649665c8",

        "colab": {

          "base_uri": "https://localhost:8080/",

          "height": 644

        }

      },

      "source": [

        "df.head(20)"

      ],

      "execution_count": 0,

      "outputs": [

        {

          "output_type": "execute_result",

          "data": {

            "text/html": [

              "<div>\n",

              "<style scoped>\n",

              "    .dataframe tbody tr th:only-of-type {\n",

              "        vertical-align: middle;\n",

              "    }\n",

              "\n",

              "    .dataframe tbody tr th {\n",

              "        vertical-align: top;\n",

              "    }\n",

              "\n",

              "    .dataframe thead th {\n",

              "        text-align: right;\n",

              "    }\n",

              "</style>\n",

              "<table border=\"1\" class=\"dataframe\">\n",

              "  <thead>\n",

              "    <tr style=\"text-align: right;\">\n",

              "      <th></th>\n",

              "      <th>Train_Image</th>\n",

              "      <th>Train_Label</th>\n",

              "    </tr>\n",

              "  </thead>\n",

              "  <tbody>\n",

              "    <tr>\n",

              "      <th>0</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00001/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>1</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00002/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>2</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00003/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>3</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00004/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>4</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00005/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>5</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00006/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>6</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00007/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>7</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00007/study...</td>\n",

              "      <td>0</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>8</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00008/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>9</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00009/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>10</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00010/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>11</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00011/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>12</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00012/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>13</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00013/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>14</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00014/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>15</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00015/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>16</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00016/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>17</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00017/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>18</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00018/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>19</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00019/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "  </tbody>\n",

              "</table>\n",

              "</div>"

            ],

            "text/plain": [

              "                                          Train_Image  Train_Label\n",

              "0   MURA-v1.1/train/XR_SHOULDER/patient00001/study...            1\n",

              "1   MURA-v1.1/train/XR_SHOULDER/patient00002/study...            1\n",

              "2   MURA-v1.1/train/XR_SHOULDER/patient00003/study...            1\n",

              "3   MURA-v1.1/train/XR_SHOULDER/patient00004/study...            1\n",

              "4   MURA-v1.1/train/XR_SHOULDER/patient00005/study...            1\n",

              "5   MURA-v1.1/train/XR_SHOULDER/patient00006/study...            1\n",

              "6   MURA-v1.1/train/XR_SHOULDER/patient00007/study...            1\n",

              "7   MURA-v1.1/train/XR_SHOULDER/patient00007/study...            0\n",

              "8   MURA-v1.1/train/XR_SHOULDER/patient00008/study...            1\n",

              "9   MURA-v1.1/train/XR_SHOULDER/patient00009/study...            1\n",

              "10  MURA-v1.1/train/XR_SHOULDER/patient00010/study...            1\n",

              "11  MURA-v1.1/train/XR_SHOULDER/patient00011/study...            1\n",

              "12  MURA-v1.1/train/XR_SHOULDER/patient00012/study...            1\n",

              "13  MURA-v1.1/train/XR_SHOULDER/patient00013/study...            1\n",

              "14  MURA-v1.1/train/XR_SHOULDER/patient00014/study...            1\n",

              "15  MURA-v1.1/train/XR_SHOULDER/patient00015/study...            1\n",

              "16  MURA-v1.1/train/XR_SHOULDER/patient00016/study...            1\n",

              "17  MURA-v1.1/train/XR_SHOULDER/patient00017/study...            1\n",

              "18  MURA-v1.1/train/XR_SHOULDER/patient00018/study...            1\n",

              "19  MURA-v1.1/train/XR_SHOULDER/patient00019/study...            1"

            ]

          },

          "metadata": {

            "tags": []

          },

          "execution_count": 8

        }

      ]

    },

    {

      "cell_type": "code",

      "metadata": {

        "id": "95cw7jEf-yn0",

        "colab_type": "code",

        "outputId": "004517d8-9caf-4324-e274-fa65f0a7fe0e",

        "colab": {

          "base_uri": "https://localhost:8080/",

          "height": 345

        }

      },

      "source": [

        "# Count labels in train set\n",

        "cases_count = df['Train_Label'].value_counts()\n",

        "print(cases_count)\n",

        "\n",

        "# Plot the results \n",

        "plt.figure(figsize=(4,4))\n",

        "sns.barplot(x=cases_count.index, y=cases_count.values)\n",

        "plt.title('Number of cases', fontsize=12)\n",

        "plt.xlabel('Case type', fontsize=10)\n",

        "plt.ylabel('Count', fontsize=10)\n",

        "plt.xticks(range(len(cases_count.index)), ['Normal(0)', 'Abnormal(1)'])\n",

        "plt.show()"

      ],

      "execution_count": 0,

      "outputs": [

        {

          "output_type": "stream",

          "text": [

            "0    8280\n",

            "1    5177\n",

            "Name: Train_Label, dtype: int64\n"

          ],

          "name": "stdout"

        },

        {

          "output_type": "display_data",

          "data": {

            "image/png": 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            "text/plain": [

              "<Figure size 288x288 with 1 Axes>"

            ]

          },

          "metadata": {

            "tags": []

          }

        }

      ]

    },

    {

      "cell_type": "code",

      "metadata": {

        "id": "oLyahcxOAXl0",

        "colab_type": "code",

        "colab": {}

      },

      "source": [

        "# Adding labels to individual images in train set\n",

        "df=pd.read_csv('MURA-v1.1/train_image_paths.csv', names=['Train_Image'])\n",

        "names=df['Train_Image'].values\n",

        "train_labels=[]\n",

        "\n",

        "for i in names:\n",

        "  if ('positive' in i):\n",

        "    train_labels.append('1')\n",

        "  elif('negative' in i):\n",

        "    train_labels.append('0')\n",

        "\n",

        "train_labels = np.array(train_labels)\n",

        "#labels = pd.DataFrame(labels, columns=['Image', 'Label'])\n",

        "df.insert(1, 'Train_Label', train_labels)\n",

        "df.to_csv('Train_set.csv', index=False)"

      ],

      "execution_count": 0,

      "outputs": []

    },

    {

      "cell_type": "code",

      "metadata": {

        "id": "7J4U3xndT3EL",

        "colab_type": "code",

        "outputId": "63d14971-0010-4992-cf4c-21ae2345bba1",

        "colab": {

          "base_uri": "https://localhost:8080/",

          "height": 644

        }

      },

      "source": [

        "df.head(20)"

      ],

      "execution_count": 0,

      "outputs": [

        {

          "output_type": "execute_result",

          "data": {

            "text/html": [

              "<div>\n",

              "<style scoped>\n",

              "    .dataframe tbody tr th:only-of-type {\n",

              "        vertical-align: middle;\n",

              "    }\n",

              "\n",

              "    .dataframe tbody tr th {\n",

              "        vertical-align: top;\n",

              "    }\n",

              "\n",

              "    .dataframe thead th {\n",

              "        text-align: right;\n",

              "    }\n",

              "</style>\n",

              "<table border=\"1\" class=\"dataframe\">\n",

              "  <thead>\n",

              "    <tr style=\"text-align: right;\">\n",

              "      <th></th>\n",

              "      <th>Train_Image</th>\n",

              "      <th>Train_Label</th>\n",

              "    </tr>\n",

              "  </thead>\n",

              "  <tbody>\n",

              "    <tr>\n",

              "      <th>0</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00001/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>1</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00001/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>2</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00001/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>3</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00002/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>4</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00002/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>5</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00002/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>6</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00003/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>7</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00003/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>8</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00003/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>9</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00004/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>10</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00004/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>11</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00004/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>12</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00004/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>13</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00005/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>14</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00006/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>15</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00006/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>16</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00007/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>17</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00007/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>18</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00007/study...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>19</th>\n",

              "      <td>MURA-v1.1/train/XR_SHOULDER/patient00007/study...</td>\n",

              "      <td>0</td>\n",

              "    </tr>\n",

              "  </tbody>\n",

              "</table>\n",

              "</div>"

            ],

            "text/plain": [

              "                                          Train_Image Train_Label\n",

              "0   MURA-v1.1/train/XR_SHOULDER/patient00001/study...           1\n",

              "1   MURA-v1.1/train/XR_SHOULDER/patient00001/study...           1\n",

              "2   MURA-v1.1/train/XR_SHOULDER/patient00001/study...           1\n",

              "3   MURA-v1.1/train/XR_SHOULDER/patient00002/study...           1\n",

              "4   MURA-v1.1/train/XR_SHOULDER/patient00002/study...           1\n",

              "5   MURA-v1.1/train/XR_SHOULDER/patient00002/study...           1\n",

              "6   MURA-v1.1/train/XR_SHOULDER/patient00003/study...           1\n",

              "7   MURA-v1.1/train/XR_SHOULDER/patient00003/study...           1\n",

              "8   MURA-v1.1/train/XR_SHOULDER/patient00003/study...           1\n",

              "9   MURA-v1.1/train/XR_SHOULDER/patient00004/study...           1\n",

              "10  MURA-v1.1/train/XR_SHOULDER/patient00004/study...           1\n",

              "11  MURA-v1.1/train/XR_SHOULDER/patient00004/study...           1\n",

              "12  MURA-v1.1/train/XR_SHOULDER/patient00004/study...           1\n",

              "13  MURA-v1.1/train/XR_SHOULDER/patient00005/study...           1\n",

              "14  MURA-v1.1/train/XR_SHOULDER/patient00006/study...           1\n",

              "15  MURA-v1.1/train/XR_SHOULDER/patient00006/study...           1\n",

              "16  MURA-v1.1/train/XR_SHOULDER/patient00007/study...           1\n",

              "17  MURA-v1.1/train/XR_SHOULDER/patient00007/study...           1\n",

              "18  MURA-v1.1/train/XR_SHOULDER/patient00007/study...           1\n",

              "19  MURA-v1.1/train/XR_SHOULDER/patient00007/study...           0"

            ]

          },

          "metadata": {

            "tags": []

          },

          "execution_count": 11

        }

      ]

    },

    {

      "cell_type": "code",

      "metadata": {

        "id": "X570i7yKIZv9",

        "colab_type": "code",

        "outputId": "11f4bf1a-c5b9-4e9e-b0a7-704f5d06b6d3",

        "colab": {

          "base_uri": "https://localhost:8080/",

          "height": 345

        }

      },

      "source": [

        "# Count individual number of labels of images in train set\n",

        "cases_count = df['Train_Label'].value_counts()\n",

        "print(cases_count)\n",

        "\n",

        "# Plot the results \n",

        "plt.figure(figsize=(4,4))\n",

        "sns.barplot(x=cases_count.index, y=cases_count.values)\n",

        "plt.title('Number of labels', fontsize=12)\n",

        "plt.xlabel('Case type', fontsize=10)\n",

        "plt.ylabel('Count', fontsize=10)\n",

        "plt.xticks(range(len(cases_count.index)), ['Normal(0)', 'Abnormal(1)'])\n",

        "plt.show()"

      ],

      "execution_count": 0,

      "outputs": [

        {

          "output_type": "stream",

          "text": [

            "0    21935\n",

            "1    14873\n",

            "Name: Train_Label, dtype: int64\n"

          ],

          "name": "stdout"

        },

        {

          "output_type": "display_data",

          "data": {

            "image/png": 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ZWXYOIjPLzkFkZtk5iMwsOweRmWXnIDKz7BxEZpadg8jMsnMQmVl2DiIzy85BZGbZOYjM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\n",

            "text/plain": [

              "<Figure size 288x288 with 1 Axes>"

            ]

          },

          "metadata": {

            "tags": []

          }

        }

      ]

    },

    {

      "cell_type": "code",

      "metadata": {

        "id": "Tm9Ge_rjpGBc",

        "colab_type": "code",

        "colab": {}

      },

      "source": [

        "#Validation set studies with labels\n",

        "df=pd.read_csv('MURA-v1.1/valid_labeled_studies.csv', names=['Valid_Image','Valid_Label'])"

      ],

      "execution_count": 0,

      "outputs": []

    },

    {

      "cell_type": "code",

      "metadata": {

        "id": "YzIs-cFopGEQ",

        "colab_type": "code",

        "outputId": "e6eae2fa-82c2-4f84-8dd4-65b4f4d1c451",

        "colab": {

          "base_uri": "https://localhost:8080/",

          "height": 644

        }

      },

      "source": [

        "df.head(20)"

      ],

      "execution_count": 0,

      "outputs": [

        {

          "output_type": "execute_result",

          "data": {

            "text/html": [

              "<div>\n",

              "<style scoped>\n",

              "    .dataframe tbody tr th:only-of-type {\n",

              "        vertical-align: middle;\n",

              "    }\n",

              "\n",

              "    .dataframe tbody tr th {\n",

              "        vertical-align: top;\n",

              "    }\n",

              "\n",

              "    .dataframe thead th {\n",

              "        text-align: right;\n",

              "    }\n",

              "</style>\n",

              "<table border=\"1\" class=\"dataframe\">\n",

              "  <thead>\n",

              "    <tr style=\"text-align: right;\">\n",

              "      <th></th>\n",

              "      <th>Valid_Image</th>\n",

              "      <th>Valid_Label</th>\n",

              "    </tr>\n",

              "  </thead>\n",

              "  <tbody>\n",

              "    <tr>\n",

              "      <th>0</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11185/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>1</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11186/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>2</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11186/study2_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>3</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11186/study3_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>4</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11187/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>5</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11188/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>6</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11189/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>7</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11190/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>8</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11191/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>9</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11192/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>10</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11193/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>11</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11194/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>12</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11195/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>13</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11196/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>14</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11197/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>15</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11198/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>16</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11199/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>17</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11199/study2_n...</td>\n",

              "      <td>0</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>18</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11200/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>19</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11201/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "  </tbody>\n",

              "</table>\n",

              "</div>"

            ],

            "text/plain": [

              "                                          Valid_Image  Valid_Label\n",

              "0   MURA-v1.1/valid/XR_WRIST/patient11185/study1_p...            1\n",

              "1   MURA-v1.1/valid/XR_WRIST/patient11186/study1_p...            1\n",

              "2   MURA-v1.1/valid/XR_WRIST/patient11186/study2_p...            1\n",

              "3   MURA-v1.1/valid/XR_WRIST/patient11186/study3_p...            1\n",

              "4   MURA-v1.1/valid/XR_WRIST/patient11187/study1_p...            1\n",

              "5   MURA-v1.1/valid/XR_WRIST/patient11188/study1_p...            1\n",

              "6   MURA-v1.1/valid/XR_WRIST/patient11189/study1_p...            1\n",

              "7   MURA-v1.1/valid/XR_WRIST/patient11190/study1_p...            1\n",

              "8   MURA-v1.1/valid/XR_WRIST/patient11191/study1_p...            1\n",

              "9   MURA-v1.1/valid/XR_WRIST/patient11192/study1_p...            1\n",

              "10  MURA-v1.1/valid/XR_WRIST/patient11193/study1_p...            1\n",

              "11  MURA-v1.1/valid/XR_WRIST/patient11194/study1_p...            1\n",

              "12  MURA-v1.1/valid/XR_WRIST/patient11195/study1_p...            1\n",

              "13  MURA-v1.1/valid/XR_WRIST/patient11196/study1_p...            1\n",

              "14  MURA-v1.1/valid/XR_WRIST/patient11197/study1_p...            1\n",

              "15  MURA-v1.1/valid/XR_WRIST/patient11198/study1_p...            1\n",

              "16  MURA-v1.1/valid/XR_WRIST/patient11199/study1_p...            1\n",

              "17  MURA-v1.1/valid/XR_WRIST/patient11199/study2_n...            0\n",

              "18  MURA-v1.1/valid/XR_WRIST/patient11200/study1_p...            1\n",

              "19  MURA-v1.1/valid/XR_WRIST/patient11201/study1_p...            1"

            ]

          },

          "metadata": {

            "tags": []

          },

          "execution_count": 14

        }

      ]

    },

    {

      "cell_type": "code",

      "metadata": {

        "id": "Jn6aeY5ypgAy",

        "colab_type": "code",

        "outputId": "2ac8ba84-7fd0-4496-a7a6-d769bb6daddd",

        "colab": {

          "base_uri": "https://localhost:8080/",

          "height": 345

        }

      },

      "source": [

        "# Count labels in validation set\n",

        "cases_count = df['Valid_Label'].value_counts()\n",

        "print(cases_count)\n",

        "\n",

        "# Plot the results \n",

        "plt.figure(figsize=(4,4))\n",

        "sns.barplot(x=cases_count.index, y=cases_count.values)\n",

        "plt.title('Number of cases', fontsize=12)\n",

        "plt.xlabel('Case type', fontsize=10)\n",

        "plt.ylabel('Count', fontsize=10)\n",

        "plt.xticks(range(len(cases_count.index)), ['Normal(0)', 'Abnormal(1)'])\n",

        "plt.show()"

      ],

      "execution_count": 0,

      "outputs": [

        {

          "output_type": "stream",

          "text": [

            "0    661\n",

            "1    538\n",

            "Name: Valid_Label, dtype: int64\n"

          ],

          "name": "stdout"

        },

        {

          "output_type": "display_data",

          "data": {

            "image/png": 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JulPSMal9mKTLJd2bHrdL7ZJ0iqR2SbdLGl9VbWZWnSp7KmuBf42IXYCJwFGSdgGOBxZF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\nlqtwBl6lAAAAAElFTkSuQmCC\n",

            "text/plain": [

              "<Figure size 288x288 with 1 Axes>"

            ]

          },

          "metadata": {

            "tags": []

          }

        }

      ]

    },

    {

      "cell_type": "code",

      "metadata": {

        "id": "Wb4jj82OpgDo",

        "colab_type": "code",

        "colab": {}

      },

      "source": [

        "# Adding labels to individual images in validation set\n",

        "df=pd.read_csv('MURA-v1.1/valid_image_paths.csv', names=['Valid_Image'])\n",

        "names=df['Valid_Image'].values\n",

        "valid_labels=[]\n",

        "\n",

        "for i in names:\n",

        "  if ('positive' in i):\n",

        "    valid_labels.append('1')\n",

        "  elif('negative' in i):\n",

        "    valid_labels.append('0')\n",

        "\n",

        "valid_labels = np.array(valid_labels)\n",

        "#labels = pd.DataFrame(labels, columns=['Image', 'Label'])\n",

        "df.insert(1, 'Valid_Label', valid_labels)\n",

        "df.to_csv('Valid_set.csv', index=False)"

      ],

      "execution_count": 0,

      "outputs": []

    },

    {

      "cell_type": "code",

      "metadata": {

        "id": "0gaA270HppEC",

        "colab_type": "code",

        "outputId": "63248922-54fd-41d7-f470-1e516600aff0",

        "colab": {

          "base_uri": "https://localhost:8080/",

          "height": 644

        }

      },

      "source": [

        "df.head(20)"

      ],

      "execution_count": 0,

      "outputs": [

        {

          "output_type": "execute_result",

          "data": {

            "text/html": [

              "<div>\n",

              "<style scoped>\n",

              "    .dataframe tbody tr th:only-of-type {\n",

              "        vertical-align: middle;\n",

              "    }\n",

              "\n",

              "    .dataframe tbody tr th {\n",

              "        vertical-align: top;\n",

              "    }\n",

              "\n",

              "    .dataframe thead th {\n",

              "        text-align: right;\n",

              "    }\n",

              "</style>\n",

              "<table border=\"1\" class=\"dataframe\">\n",

              "  <thead>\n",

              "    <tr style=\"text-align: right;\">\n",

              "      <th></th>\n",

              "      <th>Valid_Image</th>\n",

              "      <th>Valid_Label</th>\n",

              "    </tr>\n",

              "  </thead>\n",

              "  <tbody>\n",

              "    <tr>\n",

              "      <th>0</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11185/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>1</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11185/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>2</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11185/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>3</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11185/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>4</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11186/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>5</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11186/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>6</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11186/study2_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>7</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11186/study2_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>8</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11186/study2_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>9</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11186/study3_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>10</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11186/study3_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>11</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11186/study3_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>12</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11187/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>13</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11188/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>14</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11188/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>15</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11188/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>16</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11188/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>17</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11189/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>18</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11189/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "    <tr>\n",

              "      <th>19</th>\n",

              "      <td>MURA-v1.1/valid/XR_WRIST/patient11190/study1_p...</td>\n",

              "      <td>1</td>\n",

              "    </tr>\n",

              "  </tbody>\n",

              "</table>\n",

              "</div>"

            ],

            "text/plain": [

              "                                          Valid_Image Valid_Label\n",

              "0   MURA-v1.1/valid/XR_WRIST/patient11185/study1_p...           1\n",

              "1   MURA-v1.1/valid/XR_WRIST/patient11185/study1_p...           1\n",

              "2   MURA-v1.1/valid/XR_WRIST/patient11185/study1_p...           1\n",

              "3   MURA-v1.1/valid/XR_WRIST/patient11185/study1_p...           1\n",

              "4   MURA-v1.1/valid/XR_WRIST/patient11186/study1_p...           1\n",

              "5   MURA-v1.1/valid/XR_WRIST/patient11186/study1_p...           1\n",

              "6   MURA-v1.1/valid/XR_WRIST/patient11186/study2_p...           1\n",

              "7   MURA-v1.1/valid/XR_WRIST/patient11186/study2_p...           1\n",

              "8   MURA-v1.1/valid/XR_WRIST/patient11186/study2_p...           1\n",

              "9   MURA-v1.1/valid/XR_WRIST/patient11186/study3_p...           1\n",

              "10  MURA-v1.1/valid/XR_WRIST/patient11186/study3_p...           1\n",

              "11  MURA-v1.1/valid/XR_WRIST/patient11186/study3_p...           1\n",

              "12  MURA-v1.1/valid/XR_WRIST/patient11187/study1_p...           1\n",

              "13  MURA-v1.1/valid/XR_WRIST/patient11188/study1_p...           1\n",

              "14  MURA-v1.1/valid/XR_WRIST/patient11188/study1_p...           1\n",

              "15  MURA-v1.1/valid/XR_WRIST/patient11188/study1_p...           1\n",

              "16  MURA-v1.1/valid/XR_WRIST/patient11188/study1_p...           1\n",

              "17  MURA-v1.1/valid/XR_WRIST/patient11189/study1_p...           1\n",

              "18  MURA-v1.1/valid/XR_WRIST/patient11189/study1_p...           1\n",

              "19  MURA-v1.1/valid/XR_WRIST/patient11190/study1_p...           1"

            ]

          },

          "metadata": {

            "tags": []

          },