Data Science: CNN & OpenCV: Breast Cancer Detection

Explore the dataset and folder structure for breast cancer detection, noting IDC, benign vs malignant labels, class imbalance, 50x50 png patches, and per-patient folders with train, test, and val splits.

Download and use the Kaggle JSON API token to import data from Kaggle into Google Colab. Create a Kaggle account and generate the API token for setup.

Set up breast cancer detection project in Google Colab by downloading dataset via a Kaggle JSON, mounting Google Drive, and organizing folders for dataset, unzip, output, and training notebooks.

Explore how a config-driven workflow builds a breast cancer image dataset, creates train, validation, and test folders, and copies labeled images for cnn training (benign 0 vs malignant 1).

Discover how to import and configure essential libraries for a cnn breast cancer detection pipeline, including image preprocessing, data generation, model building, evaluation, and background plotting.

Plot data counts by class across train, test, and validation using a plot data function; build a data frame from benign and malignant images and visualize the distribution, noting skewness.

Plot samples from benign and malignant breast cancer images, build a train data frame with image paths and labels, and display the first five from each class in Colab.

Create a common method to count files in a directory by listing png and jpg files, recursively traversing subfolders, collecting matches in a list, and returning the list length.

Define a training plot method that takes model fit information, epochs, and a plot path to visualize training and validation loss and accuracy for breast cancer detection using CNN.

Compute class weights from the train directory to balance a skewed breast cancer dataset for cnn and opencv, using file counts, train labels, and a training-time weights dictionary.

Learn data augmentation to generate diverse data for machine learning, boosting accuracy and robustness in image-based models through padding, rotation, flipping, cropping, color changes, and noise.

Implement data augmentation for training images using an image data generator, applying rotation, zoom, shifts, shear, flips, and nearest fill, while scaling inputs to -1 to 1.

Explore data generators and real-time augmentation for memory-efficient training. Load images in batches, apply on-the-fly transformations, and feed augmented data from CPU to GPU.

Implement data generators to stream 32-image batches from a two-class breast cancer dataset (benign vs malignant) with 48x48 images, applying augmentation for training and rescaling for validation and test.

Explore convolutional neural networks (CNNs), which use convolutional layers and kernels to extract image features, then apply fully connected layers and softmax to classify images.

Explore OpenCV's role in computer vision and image processing, including face detection, object tracking, landmark detection, and malignant or benign medical image analysis, with multi-language support and practical applications.

Understand pretrained models and transfer learning using a pretrained network as a starting point alongside a custom architecture. Learn how a large dataset provides generic feature maps for image classification.

Explore ResNet50, a 50-layer convolutional neural network with skip connections that mitigate vanishing gradients and serve as a backbone for many computer vision tasks.

Explore two CNN models, a pretend model and a custom architecture, and grasp core concepts: Conv2D, filters, relu activation, batch normalization, max pooling, dropout, flattening, and dense layers.

Load ResNet50 base model without the top; attach a head with average pooling 2D, flatten, a 256-neuron dense layer with ReLU, dropout, and a 2-class soft max, freezing base layers.

Builds a custom cnn architecture with 3x3 conv blocks, channel-last inputs, batch normalization, max pooling with dropout of 0.25, and a final two-class softmax classifier.

Explore the role of optimizers in deep learning, including gradient descent, stochastic gradient descent, mini-batch gradient descent, and Adam optimizer with adaptive moment estimation, for loss minimization.

Explore the Adam optimizer, an adaptive gradient method that estimates first and second moments. It uses exponentially decaying averages to speed convergence and rectify vanishing learning rates, with computational cost.

Learn how binary cross entropy serves as a loss function, measuring the difference between predicted probabilities and actual 0/1 labels, and how optimization minimizes this cross entropy loss.

Compile the ResNet50 model with the atom optimizer, using a predefined learning rate and decay (lr divided by epochs), and evaluate with binary cross entropy loss and accuracy.

Configure and compile a custom cnn model by applying the adam optimizer with a learning-rate schedule, using binary cross entropy loss and accuracy metrics.

Examine model checkpoint callbacks to save a CNN model during training, either at epoch end or after fixed batches, tracking best performance and saving weights or the full model.

Learn to implement model checkpointing by saving the best performing model based on the lowest validation loss, using a parameterized save path and a checkpoint callback during model fit.

Explain epoch and batch size in neural network training: an epoch is a full pass over the dataset, while batch size defines how many samples update the model per step.

Learn how to fit ResNet50 and a custom CNN, using train and validation data, steps per epoch, and class weights. Use callbacks to save the best model by validation loss.