Datasets
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--- a +++ b/README.md @@ -0,0 +1,363 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.14.4 + kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 +--- + +<!-- #region --> +# 3D Image Classification + +Learn how to train a 3D convolutional neural network (3D CNN) to predict presence of pneumonia - based on [Tutorial on 3D Image Classification](https://keras.io/examples/vision/3D_image_classification/) by [Hasib Zunair](https://github.com/hasibzunair). + + +> __Dataset__: [MosMedData: Chest CT Scans with COVID-19 Related Findings Dataset](https://www.medrxiv.org/content/10.1101/2020.05.20.20100362v1) :: This dataset contains anonymised human lung computed tomography (CT) scans with COVID-19 related findings, as well as without such findings. A small subset of studies has been annotated with binary pixel masks depicting regions of interests (ground-glass opacifications and consolidations). CT scans were obtained between 1st of March, 2020 and 25th of April, 2020, and provided by municipal hospitals in Moscow, Russia. +<!-- #endregion --> + +## Verify GPU Support + +```python +# importing tensorflow +import tensorflow as tf + +device_name = tf.test.gpu_device_name() +print('Active GPU :: {}'.format(device_name)) +# Active GPU :: /device:GPU:0 +``` + +## Import Dependencies + +```python +import matplotlib.pyplot as plt +import nibabel as nib +import numpy as np +import os +import random +from scipy import ndimage +from sklearn.model_selection import train_test_split +from tensorflow import keras +from tensorflow.keras import layers +from tensorflow.keras.utils import plot_model +``` + +```python +# helper functions +from helper import (read_scan, + normalize, + resize_volume, + process_scan, + rotate, + train_preprocessing, + validation_preprocessing, + plot_slices, + build_model) +``` + +## Import Dataset + +```python +# download from https://github.com/hasibzunair/3D-image-classification-tutorial/releases/ +data_dir = './dataset' +no_pneumonia = os.path.join(data_dir, 'no_viral_pneumonia') +with_pneumonia = os.path.join(data_dir, 'with_viral_pneumonia') + +normal_scan_paths = [ + os.path.join(no_pneumonia, i) + for i in os.listdir(no_pneumonia) +] +print('INFO :: CT Scans with normal lung tissue:', len(normal_scan_paths)) + +abnormal_scan_paths = [ + os.path.join(with_pneumonia, i) + for i in os.listdir(with_pneumonia) +] +print('INFO :: CT Scans with abnormal lung tissue:', len(abnormal_scan_paths)) + +# INFO :: CT Scans with normal lung tissue: 100 +# INFO :: CT Scans with abnormal lung tissue: 100 +``` + +## Visualize Dataset + +```python +img_normal = nib.load(normal_scan_paths[0]) +img_normal_array = img_normal.get_fdata() + +img_abnormal = nib.load(abnormal_scan_paths[0]) +img_abnormal_array = img_abnormal.get_fdata() + +plt.figure(figsize=(30,10)) + +for i in range(6): + plt.subplot(2, 6, i+1) + plt.imshow(img_normal_array[:, :, i], cmap='Blues') + plt.axis('off') + plt.title('Slice {} - Normal'.format(i)) + + plt.subplot(2, 6, 6+i+1) + plt.imshow(img_abnormal_array[:, :, i], cmap='Reds') + plt.axis('off') + plt.title('Slice {} - Abnormal'.format(i)) +``` + + + + +## Data Pre-processing + +### Normalization + +```python +# Read and process the scans. +# Each scan is resized across height, width, and depth and rescaled. +abnormal_scans = np.array([process_scan(path) for path in abnormal_scan_paths]) +normal_scans = np.array([process_scan(path) for path in normal_scan_paths]) +``` + +```python +# For the CT scans having presence of viral pneumonia +# assign 1, for the normal ones assign 0. +abnormal_labels = np.array([1 for _ in range(len(abnormal_scans))]) +normal_labels = np.array([0 for _ in range(len(normal_scans))]) +``` + +### Train Test Split + +```python +X = np.concatenate((abnormal_scans, normal_scans), axis=0) +Y = np.concatenate((abnormal_labels, normal_labels), axis=0) + +x_train, x_val, y_train, y_val = train_test_split(X, Y, test_size=0.3, random_state=42) +print('INFO :: Train / Test Samples - %d / %d' % (x_train.shape[0], x_val.shape[0])) +# INFO :: Train / Test Samples - 140 / 60 +``` + +### Data Augmentation + + +#### Data Loader + +```python +# Define data loaders. +train_loader = tf.data.Dataset.from_tensor_slices((x_train, y_train)) +validation_loader = tf.data.Dataset.from_tensor_slices((x_val, y_val)) +batch_size = 2 + +# Augment the on the fly during training. +train_dataset = ( + train_loader.shuffle(len(x_train)) + .map(train_preprocessing) + .batch(batch_size) + .prefetch(2) +) +# Only rescale. +validation_dataset = ( + validation_loader.shuffle(len(x_val)) + .map(validation_preprocessing) + .batch(batch_size) + .prefetch(2) +) +``` + +### Visualizing Augmented Datasets + +```python +data = train_dataset.take(1) +images, labels = list(data)[0] +images = images.numpy() +image = images[0] +print("CT Scan Dims:", image.shape) +# CT Scan Dims: (128, 128, 64, 1) +plt.imshow(np.squeeze(image[:, :, 30]), cmap="gray") + +# Visualize montage of slices. +# 4 rows and 10 columns for 100 slices of the CT scan. +plot_slices(4, 10, 128, 128, image[:, :, :40]) +``` + + + + + + +## Building the Model + +```python +model = build_model(width=128, height=128, depth=64) +model.summary() +``` + +<!-- #region --> +```bash +Model: "3dctscan" +_________________________________________________________________ + Layer (type) Output Shape Param # +================================================================= + input_3 (InputLayer) [(None, 128, 128, 64, 1) 0 + ] + + conv3d_9 (Conv3D) (None, 126, 126, 62, 64) 1792 + + max_pooling3d_8 (MaxPooling (None, 63, 63, 31, 64) 0 + 3D) + + batch_normalization_8 (Batc (None, 63, 63, 31, 64) 256 + hNormalization) + + conv3d_10 (Conv3D) (None, 61, 61, 29, 64) 110656 + + max_pooling3d_9 (MaxPooling (None, 30, 30, 14, 64) 0 + 3D) + + batch_normalization_9 (Batc (None, 30, 30, 14, 64) 256 + hNormalization) + + conv3d_11 (Conv3D) (None, 28, 28, 12, 128) 221312 + + max_pooling3d_10 (MaxPoolin (None, 14, 14, 6, 128) 0 + g3D) + + batch_normalization_10 (Bat (None, 14, 14, 6, 128) 512 + chNormalization) + + conv3d_12 (Conv3D) (None, 12, 12, 4, 256) 884992 + + max_pooling3d_11 (MaxPoolin (None, 6, 6, 2, 256) 0 + g3D) + + batch_normalization_11 (Bat (None, 6, 6, 2, 256) 1024 + chNormalization) + + global_average_pooling3d_1 (None, 256) 0 + (GlobalAveragePooling3D) + + dense_2 (Dense) (None, 512) 131584 + + dropout_1 (Dropout) (None, 512) 0 + + dense_3 (Dense) (None, 1) 513 + +================================================================= +Total params: 1,352,897 +Trainable params: 1,351,873 +Non-trainable params: 1,024 +_________________________________________________________________ +``` +<!-- #endregion --> + +### Compile the Model + +```python +initial_learning_rate = 0.0001 + +lr_schedule = keras.optimizers.schedules.ExponentialDecay( + initial_learning_rate, + decay_steps=100000, + decay_rate=0.96, + staircase=True) + +model.compile( + loss='binary_crossentropy', + optimizer=keras.optimizers.Adam(learning_rate=lr_schedule), + metrics=['acc'] +) +``` + +### Callbacks + +```python +cp_cb = keras.callbacks.ModelCheckpoint( + './checkpoints/3dct_weights.h5', + save_best_only=True +) + +es_cb = keras.callbacks.EarlyStopping( + monitor='val_acc', + patience=15 +) +``` + +## Model Training + +```python +epochs = 100 + +model.fit( + train_dataset, + validation_data=validation_dataset, + epochs=epochs, + shuffle=True, + verbose=2, + callbacks=[cp_cb, es_cb] +) +# Epoch 46/100 +# 70/70 - 22s - loss: 0.3383 - acc: 0.8429 - val_loss: 0.8225 - val_acc: 0.6833 - 22s/epoch - 313ms/step +``` + +### Visualizing Model Performance + +```python +fig, ax = plt.subplots(1, 2, figsize=(20, 3)) +ax = ax.ravel() + +for i, metric in enumerate(["acc", "loss"]): + ax[i].plot(model.history.history[metric]) + ax[i].plot(model.history.history["val_" + metric]) + ax[i].set_title("Model {}".format(metric)) + ax[i].set_xlabel("epochs") + ax[i].set_ylabel(metric) + ax[i].legend(["train", "val"]) +``` + + + + +## Loading Best Training Weights + +```python +model.load_weights('./checkpoints/3dct_weights.h5') +``` + +## Make Predictions + +```python +pred_dataset = './predictions' +pred_paths = [os.path.join(pred_dataset, i) for i in os.listdir(pred_dataset)] + +z_val = np.array([process_scan(path) for path in pred_paths]) + +for i in range(len(z_val)): + prediction = model.predict(np.expand_dims(z_val[i], axis=0))[0] + scores = [1 - prediction[0], prediction[0]] + class_names = ['normal', 'abnormal'] + +pred_image = nib.load(pred_paths[i]) +pred_image_data = pred_image.get_fdata() + +normal_class = class_names[0], round(100*scores[0], 2) +abnormal_class = class_names[1], round(100*scores[1], 2) +annotation = normal_class + abnormal_class + +plt.imshow(pred_image_data[:,:, pred_image_data.shape[2]//2], cmap='gray') +plt.title(os.path.basename(pred_paths[i])) +plt.xlabel(annotation) +plt.show() +``` + + + +```python +print(scores, class_names) +``` + +```python + +```