Work

An optimized hybrid deep learning model to detect Alzheimer disease

Year: 2025

Type: article

Abstract: Alzheimer's is a serious neurodegenerative disease that requires early detection for effective intervention. Traditional methods often struggle with accurately identifying the early stages, such as mild cognitive impairment (MCI), due to limitations in feature extraction and classification. To address these challenges, we present an optimized hybrid deep learning model for Alzheimer's disease detection. Our model employs the Inception v3 algorithm for initial feature extraction and the ResNet 50 algorithm for classification. Additionally, we optimize the network parameters using the Adaptive Rider Optimization (ARO) algorithm to enhance detection performance. Experimental analysis using a benchmark dementia dataset demonstrates that our model achieves superior accuracy of 96.6%, precision of 98%, recall of 97%, and F1-score of 98%, outperforming state-of-the-art techniques.

Source: Scientific Reports

DOI: 10.1038/s41598-025-14169-8

Authors: A. Sundar Raj, C. Gunasundari, S. Senthilkumar, S. Sivamani

Institutions: E.G.S. Pillay Engineering College, SRM Institute of Science and Technology

Language: English

Topic: Dementia and Cognitive Impairment Research

Subfield: Psychiatry and Mental health

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,690