Developing Computer Aided algorithm for the diagnosis in histopathological images to classify benign vs malignant using deep learning approach

The main objective of this project was to design a Computer Aided Diagnosis system to develop a deep learning algorithm for diagnosis in histological patches to classify benign vs malignant patches. System architecture- Image → Normalization → Per instance standardization → Aggressive Data Augmentation → Classification. Steps- 1) Data normalization, 2) Per instance standardization (Standardization by mean and std of the training dataset), 3) Aggressive Data Augmentation (Horizontal flip, Vertical flip), and 4) Classification; Best performing classification model- ResnNet50 (ImageNet-pretrained).