Learn 3D Image Classification with Python and Keras

Create and organize a 3d_classification folder in Google Drive to start a 3d image classification project with Python and Keras, preparing folders and files.

Explore how Python notebooks (.ipynb) enable interactive code, sharing, and real-time visualization in Google Colab for 3D image classification projects, including downloading and uploading code.ipynb to the 3d_classification folder.

Learn how a pre-trained 3d convolutional neural network trained on ct scans accelerates 3d image classification, using ct_weights.h5 as a starting point.

Use your trained .h5 model to classify 3D images from the prediction folder in the 3d_classification project, including CT scans. Run the predict function to receive the model's classification labels.

Enable the GPU in Google Colab to accelerate 3D image classification with Python and Keras. Learn to change runtime type and select hardware accelerator to activate GPU.

Verify GPU availability in Google Colab by running a TensorFlow script, ensuring a 'found GPU' result, and learn CPU fallback during 3D image classification with Python and Keras.

Connect Google Colab with Google Drive to mount your drive and access the dataset for training a 3D image classification model in Python and Keras.

Import essential libraries for a ct scan deep learning project, including numpy, nibble, random, tensorflow, keras, matplotlib.pyplot, scipy, ndimage, plot_model, and train_test_split for data preparation and training and validation split.

Specify directory paths for CT scan images with os.path.join, creating base_dir/ct_scans and two subfolders—no viral pneumonia and with viral pneumonia—for portable, flexible code.

Count normal and abnormal CT scans to reveal data distribution and balance for preprocessing and analysis in 3d image classification with python and keras.

Visualize how CT scans with and without pneumonia differ by loading two CT images with nibabel, extracting data, and plotting twelve grayscale slices to reveal signs of pneumonia.

Perform pre-processing of ct scan image data in nifti format by normalizing and resizing volumes to 64x128x128, clipping values between -1000 and 400, scaling to 0-1, and converting to float32.

Create binary labels for CT scans to distinguish normal from viral pneumonia using numpy arrays. Abnormal_labels holds ones for pneumonia, normal_labels holds zeros for normal, enabling supervised learning.

Concatenate abnormal and normal scans into x and y arrays, then split with train_test_split using test_size=0.3. Use random_state for reproducibility and print x_train, y_train, x_val, y_val shapes.

Apply data preprocessing and augmentation to CT scan volumes with rotate-based augmentation, add channel dimensions, and build efficient tf.data pipelines for training and validation.

Visualize augmented ct scans to understand augmentation effects on the training data for 3d image classification with Python and Keras.

Build a 3D CNN in Keras with 64 filters, kernel size 3, relu activations, repeated conv–pool–batch norm blocks, global average pooling, dense 512 units with relu, dropout, and sigmoid output.

Visualize the 3D convolutional neural network architecture using the Keras plot_model function to reveal layers, input shapes, and parameter counts for 3D image classification.

Compile the model for binary classification with binary cross entropy loss, the Adam optimizer, and an exponential decay schedule with staircase updates; monitor accuracy during training.

Explore two Keras callbacks: modal checkpoint saves weights to CT Weights dot h5 with save_best_only true, and early stopping halts after 15 epochs of no validation improvement.

Train a predefined 3D convolutional neural network with Keras using fit on the train dataset, with validation data, 100 epochs, shuffle, and model checkpoint and early stopping (CT_Weights.h5).

Load the best training weights saved in ct_weights.h5 using the checkpoint callback and model.load_weights to ensure robust performance on new data and reduce overfitting.

A practical walkthrough of predicting CT scan classifications with a trained model, processing scans, generating probability scores for normal and abnormal classes, and visualizing results.