Developing Computer Aided Algorithm for the Diagnosis in Dermoscopic Images to Classify Melanoma vs All Other Types Using Deep Learning Approach

The main goal of this project was to design a computer-aided diagnosis system to classify dermoscopic images and determine whether any case belongs to Nevus or lesion. 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 (Rotation, Width and Height shift, Zooming, Flipping, Brightness), 4) Classification; Best performing classification model - Ensemble of Inception ResNetv2, Inception v3, EfficientNet B3 (ImageNet-pretrained).