Alzheimer’s Disease Detection Using Lightweight Convolutional Neural Network on MRI Scans
DOI:
https://doi.org/10.54536/ajbb.v4i1.5943Keywords:
Deep Learning, Detection, Disease Classification, Early Detection, Kaggle Dataset, Medical Images AnalysisAbstract
This research suggests a lightweight Convolutional Neural Network (CNN) model for Alzheimer’s Disease (AD) detection and classification from Magnetic Resonance Imaging (MRI) scans. Two models were trained: a binary model distinguishing AD from non-AD cases and a multi-class model distinguishing four stages of dementia (Non-Demented, Very Mild-Demented, Mild Demented, and Moderate Demented). The models were trained on a Kaggle dataset with high accuracy (97.5% binary, 89.3% multi-class). The research demonstrates the viability of lightweight CNNs for low-cost and precise AD diagnosis, with class imbalance managed through data augmentation.
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Copyright (c) 2025 Venant Niyonkuru, Sekou Sylla, Zindazed Abdshahd Kasuli, Jimmy Jackson Sinzinkayo, Deo Kabanga
This work is licensed under a Creative Commons Attribution 4.0 International License.
