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+## Pneumonia Detection from Chest X-Ray Images                                         
+
+<pre>
+Domain             : Computer Vision, Machine Learning
+Sub-Domain         : Deep Learning, Image Recognition
+Techniques         : Deep Convolutional Neural Network, ImageNet, Inception
+Application        : Image Recognition, Image Classification, Medical Imaging
+</pre>
+
+### Description
+<pre>
+1. Detected Pneumonia from Chest X-Ray images using Custom Deep Convololutional Neural Network and by retraining pretrained model “InceptionV3” with 5856 images of X-ray (1.15GB).
+2. For retraining removed output layers, freezed first few layers and fine-tuned model for two new label classes (Pneumonia and Normal).
+3. With Custom Deep Convololutional Neural Network attained testing accuracy 89.53% and loss 0.41.
+</pre>
+
+
+
+<b>Model Parameters</b>
+Machine Learning Library: Keras
+Base Model              : InceptionV3 && Custom Deep Convolutional Neural Network
+Optimizers              : Adam
+Loss Function           : categorical_crossentropy
+
+<b>For Custom Deep Convolutional Neural Network : </b>
+<b>Training Parameters</b>
+Batch Size              : 64
+Number of Epochs        : 30
+Training Time           : 2 Hours
+
+<b>Output (Prediction/ Recognition / Classification Metrics)</b>
+<b>Testing</b>
+Accuracy (F-1) Score    : 89.53%
+Loss                    : 0.41
+Precision               : 88.37%
+Recall (Pneumonia)      : 95.48% (For positive class)
+<!--Specificity             : -->
+</pre>
+
+
+<kbd>
+<a href="[https://i.imgur.com/km4MF3J.png](https://imgur.com/km4MF3J)"></a>
+</kbd>
+
+
+
+#### Tools / Libraries
+<pre>
+Languages               : Python
+Tools/IDE               : Google Colab
+Libraries               : Keras, TensorFlow, Inception, ImageNet
+</pre>
+