A 2D-CNN Based System for the Classification of Alzheimer’s Disease Using Brain MRI Scans
- DOI
- 10.2991/978-94-6463-700-7_2How to use a DOI?
- Keywords
- Alzheimer’s disease; Convolutional Neural Network; MRI scans
- Abstract
Alzheimer’s disease (AD) is a commonly spread brain illness. A computer-assisted method becomes vital for accurate and timely AD categorization. Deep learning algorithms offer substantial advantages over machine learning techniques. The execution of deep learning is extremely efficient in the area of AD diagnosis from MR brain scans. The principal aim of the projected model is to discover the outcome using a Convolutional Neural Network (CNN) applied to the MR scans for AD classification. A variety of CNN architectures are available. The projected model is an implementation of basic construction with a change as per the convolutional layer’s kernel size and its strides. The study proposes a sequential CNN system for AD categorization. The proposed system reached 98.78% classification accuracy on the ‘Alzheimer’s Disease Neuroimaging Initiative (ADNI)’ dataset for the multiclass categorization of AD. The outcomes were promising for the detection of AD.
- 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.
Cite this article
TY - CONF AU - Atul Mathur AU - Rakesh Kumar Dwivedi AU - Rajul Rastogi PY - 2025 DA - 2025/04/19 TI - A 2D-CNN Based System for the Classification of Alzheimer’s Disease Using Brain MRI Scans BT - Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025) PB - Atlantis Press SP - 4 EP - 16 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-700-7_2 DO - 10.2991/978-94-6463-700-7_2 ID - Mathur2025 ER -