CNN Alzheimer’s Disease Prediction Using MRI Pictures

DOI

10.2991/978-94-6463-948-3_23How to use a DOI?

Keywords

Alzheimer’s disease; Magnetic Resonance Imaging (MRI); Deep Learning; Convolutional neural networks (CNNs)

Abstract

The progressive loss of neurons that results in dementia is a trademark of Alzheimer’s disease (AD). AD growth is linked to structural alterations in the brain that can be studied with magnetic resonance imaging (MRI). Deep learning techniques have recently confirmed potential in the prediction of AD from MRI. Learning discriminative features from MRIs for classification is a good fit for convolutional neural networks (CNNs). Significant strides partake been made in the fields of healthcare and medical science thanks to deep learning. Its uses span from drug development and discovery to patient diagnosis and treatment optimization, including medical picture examination. Convolutional neural networks (CNNs), a deep learning technique, are being used more and more to predict Alzheimer’s disease from structural MRI brain images. CNNs are trained on MRI patches or voxels to extract discriminative features and classify the state of the disease. The use of deep learning in computer-aided MRI diagnosis of Alzheimer’s disease shows potential. Although diagnosing or predicting Alzheimer’s disease is a difficult endeavor, there are a number of methods and approaches now in use. The accuracy, invasiveness, expense, and practicality of these procedures differ. Several of these techniques entail procedures like neuro-imaging, which lacks specificity, and biomarker analysis, which necessitates opening the skull. However, by using Deep Learning techniques like CNNs, we may not only streamline the procedure but also achieve cutting-edge precision in outcome prediction. Furthermore, the accuracy ranges from 85% to 90% in contrast to the 65% accuracy of conventional approaches.

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  - P. Rahul Das
AU  - Mudavathraju
AU  - K. Krishnajyothi
AU  - G. Kalayani
PY  - 2026
DA  - 2026/01/06
TI  - CNN Alzheimer’s Disease Prediction Using MRI Pictures
BT  - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
PB  - Atlantis Press
SP  - 326
EP  - 334
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-948-3_23
DO  - 10.2991/978-94-6463-948-3_23
ID  - Das2026
ER  -