Title: Alzheimer's disease classification using the fusion of improved 3D-VGG-16 and machine learning classifiers

Authors: Priyanka Gautam; Manjeet Singh

Addresses: ECE Department, Dr B.R. Ambedkar National Institute of Technology, Jalandhar, 144008, Punjab, India ' ECE Department, Dr B.R. Ambedkar National Institute of Technology, Jalandhar, 144008, Punjab, India

Abstract: Alzheimer's disease is a significant cause of mortality in older adults. It is characterised by the accumulation of amyloid-β (Aβ) and hyperphosphorylated tau in the brain. Structural magnetic resonance imaging (MRI) can detect these progressive brain changes in the early stages. This work presented an improved 3D-VGG-16 deep neural network model fused with three machine learning classifiers: GPC, SVM, and RF. The improved 3D-VGG-16 incorporated the dropout technique, removed fully connected layers, and replaced them with new ones. To train and test T1-weighted 3D MRI images were utilised, a total of 445 images where 239 (AD-69, CN-170) from ADNI and 206 (AD-136, HC-70) from MIRIAD. The models achieved accuracies of 98.75%, 97.5%, and 93.75% (ADNI), and 96.25%, 93.75%, and 91.25% (MIRIAD) for 3D-VGG-16+GPC, 3D-VGG-16+SVM, and 3D-VGG-16+RF. The proposed model with dropout, 3D data adaption, and simultaneous data evaluation demonstrated accurate AD classification, especially with the GPC classifier.

Keywords: deep learning; Alzheimer's disease; magnetic resonance imaging; MRI; machine learning; ADNI; MIRIAD.

DOI: 10.1504/IJBET.2025.143776

International Journal of Biomedical Engineering and Technology, 2025 Vol.47 No.1, pp.1 - 27

Received: 23 Apr 2024
Accepted: 26 Jun 2024
Published online: 06 Jan 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article