Computer Aided algorithm

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).

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 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).