Classification of Alzheimer’s Disease Using Deep Learning Methodologies on MR Images

Alzheimer’s disease (AD) is a progressive neurological disorder. In India, there are over four million dementia patients. Given that dementia affects at least 43 million individuals worldwide, the condition is a global health emergency that requires attention. The elderly are frequently affected by this illness. It influences cognitive cells, which leads to serious health concerns that are related to memory. As a result, for prompt treatment and improved patient outcomes, early and correct AD diagnosis is essential. Recently, deep learning methodologies have brought about positive developments to diagnose AD in the light of the quick advancements in neuroimaging techniques. The goal is to use convolutional neural network (CNN) architectures to create models (custom-built CNN Model, MobileNetV2, and DenseNet169) that aid in diagnosing the illness. A data augmentation approach is used to address the issue regarding the limited size of the Kaggle dataset. The stage of AD can be categorized based on the output of the CNN models. After analyzing the proposed models using evaluation metrics such as accuracy and precision, the accuracies of the custom-built CNN model, MobileNetV2, and DenseNet169 are obtained as 94.35%, 80.49%, and 73.07%, respectively.