Detection and Classification of Alzheimer’s Disease Through Brain MRI Imaging
Abstract
Significant progress has been made in diagnosing Alzheimer's disease (AD) via MRI image processing, with deep learning methods being essential. In this work, we use the ADNI dataset to investigate novel approaches for MRI image-based AD detection and classification. Convolutional Neural Networks (CNN), ResNet, and InceptionV3 models have been shown to be effective in earlier studies; CNN achieved an astounding 96.7% accuracy rate. To improve performance even more, we present the Xception model for categorization, which achieves an impressive 99% accuracy rate.
By utilizing Xception's capabilities, we are able to identify patterns associated with AD in MRI images with greater accuracy, improving diagnostic precision. Our results highlight the potential of using a variety of deep learning architectures for Alzheimer's identification, with Xception showing promise as a method for increasing MRI image classification accuracy. This study supports further attempts to create strong and trustworthy instruments for early intervention strategies for Alzheimer’s and dementia care.
Downloads
References
Xiaohong C, Jie X, Hao G, Guimei Y, Huijun Z, Fangpeng L, et al. Classification of Alzheimer's disease, mild cognitive impairment, and normal controls with sub network selection and graph kernel principal component analysis based on minimum spanning tree brain functional network. Front Comput Neurosci Methods 2018;12(31):1–11.
Ashraf J, Ahmad J, Ali A, Zaheer UH. Analyzing the behavior of neuronal pathways in Alzheimer's disease using petri net modeling approach. Front Neuroinform 2018;12(26):1–24.
Tijn MS, Koini M, Vosa F, Seiler S, Rooij M, Lechner A. Individual classification of Alzheimer's disease with diffusion magnetic resonance imaging. NeuroImage. 152. Elsevier; 2017. p. 476–81.
Vos F, Koini M, Tijn MS, Seiler S, Grond J, Lechner A. A comprehensive analysis of resting state fMRI measures to classify individual patients with Alzheimer's disease. NeuroImage. 167. Elsevier; 2018. p. 62–72.
Thies W, Bleiler L. 2013 Alzheimer's facts and figures, Alzheimer's $ dementia. J Alzheimer's Assoc 2013;9(2):208–45.
Ouyang YC, Chen HM, Chai JW, Chen C, Poon SK, Yang CW, et al. Band expansion based over complete independent component analysis for multispectral processing of magnetic resonance images. IEEE Trans Biomed Eng 2008;55(6):1666–77.
Ouyang YC, Chen HM, Chen C, Poon SK, Yang CW, Lee SK. Independent component analysis for magnetic resonance image analysis. EURASIP J. on Adv. in Sig. Proc., Hindawi 2008;2008(780656):1–14.
Cocosco CA, Zijdenbos AP, Evans AC. A fully automatic and robust brain MRI tissue classification method. Med. Imag. Anal. 2003;7(4):513–27. Elsevier.
Wang J, Chang CI. Independent component analysis-based dimensionality reduction with applications in hyper spectral image analysis. IEEE Trans Geo Remote Sens 2006;44(6):1586–600.
Lenzi D, Serra L, Perri R, Pantano P, Lenzi GL, Paulesu E, et al. Single domain amnestic MCI: a multiple cognitive domains fMRI investigation. Neurobiol. Aging 2011;32(9):1542–57. Elsevier.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 T. Jalaja, T. Adilakshmi, Kyasa Vidhyadhary
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
