Developing Computer Aided Algorithm for the Diagnosis in Histopathological Images to Classify Benign vs Malignant Using Deep Learning Approach
Project for ‘Computer Aided Diagnosis’ course ( Master’s Third Semester at University of Girona, January 2020)
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 used - 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).