Alzheimer's Disease phase detection using MRI images:

In this project, I applied transfer learning on both pre-trained ResNet50 and VGG19 DNNs to detect various phases of Alzheimer’s disease using MRI images. The dataset that was used was ADNI Dataset, including 100 patients and 100 normal subjects. The input to the ConvNets were GM-segmented brain images. The extracted features from ConvNets were input to a linear SVM classifier. The best performance in accuracy and specificity belonged to VGG19 architecture, with values of 92.5% and 93%, respectively.

Video processing on a fish-eyed camera for human detection using Transfer Learning on Yolov4

CIFAR-100 classification using Transfer Learning on Resnet50