Alzheimer’s Detection in 3D Magnetic Resonance Imaging Using Deep Convolutional Neural Networks

Augusto Berwaldt de Oliveira; Claudio Resin Geyer

doi:10.5753/jbcs.2026.6230

Authors

Augusto Berwaldt de Oliveira Federal University of Rio Grande do Sul https://orcid.org/0000-0001-7315-6813
Claudio Resin Geyer Federal University of Rio Grande do Sul https://orcid.org/0000-0002-8602-2336

DOI:

https://doi.org/10.5753/jbcs.2026.6230

Keywords:

Machine Learning, Alzheimer, Computer Vision, Deep Learning, Image Processing, CNN, MRI

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide and poses significant challenges for early and accurate diagnosis. In this paper, we present a comparative study between two-dimensional (2D) and three-dimensional (3D) convolutional neural networks (CNNs) for Alzheimer’s disease detection using T1-weighted magnetic resonance imaging (MRI). A robust preprocessing pipeline based on the MNI-152 atlas is employed, including spatial normalization, skull stripping, and intensity normalization, ensuring anatomical consistency across subjects. A key contribution of this work is the adaptation of transfer learning from 2D to 3D CNNs, achieved by volumetrically extending pretrained 2D convolutional filters to initialize 3D kernels, improving convergence and feature representation in volumetric models. The proposed approach is evaluated on 2,603 MRI volumes from the ADNI dataset using a controlled experimental setup with identical preprocessing, training, and validation procedures for both models. Experimental results show that the 2D CNN achieved higher classification accuracy (90.17%) compared to the 3D CNN (87.88%), while both approaches demonstrated strong potential for MRI-based AD detection. These findings provide practical insights into the trade-offs between slice-based and volumetric CNN architectures in neuroimaging applications.

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