Deep Learning-based Alzheimer's Detection: A Novel Approach

Alzheimer's disease is a gradually increasing neu- rodegenerative disorder, it presents its manifestations with cognitive decline and brain degeneration, above all in aged individuals. Such early diagnosis is paramount precisely because of the irreversibility of its progression, an urgent clinical, social, and economic need. This study puts forward deep learning methods or approaches in the scenario of MRI data in detecting and classifying AD at different stages, such as Non-Demented, Mildly Demented, Moderately Demented, and Severely Demented. The results were exceptional with the CNNs, autoencoders, and transfer learning reporting up to 99.68% classification accuracy by the models. Based on these, this paper introduces a new hybrid framework that incorporates the stacked sparse autoencoders which is used for deep feature extraction and transfer learning with Inception-V4 CNNs. The proposed approach encompasses MRI preprocessing using elastic deformations, sensitivity ana lysis of brain Regions of Interest, and ensemble classification techniques that ensure high accuracy with less complexity and improved interpretability. This framework brings early-stage AD detection to a new level, addresses the current challenges, and hopefully promises better clinical, social, and economic outcomes.