An Android-Assisted Accurate Transfer Learning Framework for Alzheimer’s Disease Diagnosis

Alzheimer’s disease is a progressive and devastating neurodegenerative disease caused by the shrinkage of brain tissues, which leads to memory loss, cognitive decline, and other symptoms, and its diagnosis needs magnetic resonance imaging scans of the brain. This incurable disease can be deadly if proper treatment and lifestyle are not followed. Early and accurate diagnosis of this disease is crucial, but its magnetic resonance imaging-based traditional diagnosis is cumbersome and costly. Efficient automated diagnosis is highly required for modernizing the treatment and management of this disease. This study aims to develop an Android application based on the transfer learning approach, where the performance of EfficientNet-B3 and VGG16 were evaluated to select a effective model for accurately diagnosing four stages of Alzheimer’s disease, such as non-demented, very mild demented, mild demented, and moderate demented. The both models were utilized with a dataset of 12800 magnetic resonance imaging images through the transfer learning approach, where the EfficientNet-B3 outperformed VGG16 model and achieved 97.14% accuracy, which indicated its efficiency in diagnosing Alzheimer’s disease. After several evaluations, the EfficientNet-B3 model was integrated with an Android application for making Alzheimer’s disease diagnosis easier and cheaper than the traditional approach. Besides diagnosis, it has several features for enhancing the quality of life of patients with Alzheimer’s disease.