Optimizing Deep Learning Models for Alzheimer’s Disease Diagnosis Using MRI Scans

DOI

10.2991/978-94-6463-858-5_2How to use a DOI?

Keywords

Alzheimer’s disease; MRI; Deep Transfer Learning; MobileNet; CNN; ADASYN; Dementia Diagnosis

Abstract

Alzheimer’s Disease (AD) is a degenerative neurological disorder causing progressive memory loss and cognitive decline. This research focuses on improving AD diagnosis using deep transfer learning with pre-trained CNN models like MobileNet, ResNet, and VGG. By applying MRI image preprocessing, data augmentation, and ADASYN sampling, the study enhances detection accuracy while addressing dataset imbalance. MobileNet is selected for its low computational demand and suitability for mobile deployment, making this model clinically accessible and scalable. Results suggest the proposed approach significantly improves early AD detection, ensuring better intervention and patient care.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - B. Vasantha Rani
AU  - K. Dhana Lakshmi
AU  - M. Anju Abhinaya
AU  - A. Keerthana
PY  - 2025
DA  - 2025/11/04
TI  - Optimizing Deep Learning Models for Alzheimer’s Disease Diagnosis Using MRI Scans
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 5
EP  - 14
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_2
DO  - 10.2991/978-94-6463-858-5_2
ID  - Rani2025
ER  -