BCD: Breast Cancer Detection

The original dataset contains a total of 745 images, which are divided into two classes: Cancer and Non_Cancer. We divided the dataset into standard proportions for training (70%), validation (20%), and testing (10%). To increase dataset diversity and improve the model’s generalization ability, we used various augmentation techniques, such as horizontal and vertical flipping, rotation from -5° to +5°. We then tripled (3x) the number of training images. After that, the dataset has been expanded to 1725 images.