Comparative Assessment among Different Convolutional Neural Network Architectures for Alzheimer’s Disease Detection
Gargi Sharma, Ankit Vijayvargiya, Rajesh Kumar
Abstract
Alzheimer’s Disease(AD) is a type of Dementia affecting the brain cells. An intense amount of research has been done on the subject of Alzheimer’s Disease Detection and even now huge amounts of research are going on towards this subject. Over time different deep learning models have been implemented including various transfer learning models. In this work a comprehensive analysis of eight transfer learning models has been done to classify AD in 4 classes; Non-Demented, Very Mild-Demented, Mild-Demented and Moderate Demented. The transfer learning models implemented here are: DenseNet-169, Inception ResNet-V2, MobileNet-V2, ResNet-101, Inception-V3, ResNet-50, VGG-16, VGG-19. The transfer learning model with the highest scores gave an accuracy of 98.0%and precision of 98.01%, which is a good score for medical imaging problems.