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All Journal Energy: Jurnal Ilmiah Ilmu-ilmu Teknik
Bagas Martinus Rianu
Magister Program of Electrical Engineering, Kanazawa University, 920-1192, Japan
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Exploring 3D Convolutional Neural Network Models for Alzheimer’s Disease Classification Based on 3D MRI Images Titus Batlayeri; Subairi Subairi; Rahman Arifuddin; Bagas Martinus Rianu
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK Vol. 15 No. 2 (2025): ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (July-November 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.v15i2.15208

Abstract

Alzheimer’s disease is a common form of progressive dementia, especially among the elderly, and is characterized by a decline in cognitive function. Classifying this disease using 3D brain imaging through MRI is challenging due to the complexity of the data and the similarity of features across classes. This study develops a classification model based on a 3D Convolutional Neural Network (3D CNN) architecture, specifically using ResNet-18. The dataset used is obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), consisting of 1,281 samples evenly distributed across three classes: Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and Alzheimer’s Disease (AD). The data undergo several preprocessing steps, including skull stripping, normalization, and augmentation. The model is tested in two configurations: without dropout and with a dropout rate of 0.3. The results show that the model with dropout performs better, achieving a classification accuracy of 62.0% and a macro F1-score of 0.604. The model outperforms ADNet and Vision Transformer, and approaches the accuracy of Vision Mamba. Nevertheless, this approach still requires further development, particularly in improving accuracy for the CN class and reducing performance imbalance across classes.
Co-Authors Rahman Arifuddin Subairi Subairi Titus Batlayeri