CNN Models Performance Analysis on MRI images of OASIS dataset for distinction between Healthy and Alzheimer's patient

Abstract

Here in this paper we present the performance result of pretrained model trained on natural Image and its result in medical image classification. Besides, scratch trained model is also trained from available medical MRI images, in order to have a comparative analysis. We have performed shallow tuning and fine tuning of pretrained model (Alexnet, Googlenet, and Resnet50) in a bunch of layers in order to find the impact of each section of layers in classification result. We have used 28 Normal controls (NC) and 28 Alzheimer's disease (AD) patients for classification, selecting 30 important slices from each patient. Once all the slices are collected, each model was trained, validated and tested in ratio of 6:2:2 on random selection basis. The resulting testing results are reported and analyzed. So, that the final CNN model was built with minimal number of layers for optimal performance.