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3D MRI-Based Alzheimer's Disease Classification Using Multi-Modal 3D CNN with Leakage-Aware Subject-Level Evaluation

Md Sifat, Sania Akter, Akif Islam, Md. Ekramul Hamid, Abu Saleh Musa Miah, Najmul Hassan, Md Abdur Rahim, Jungpil Shin

Abstract

Deep learning has become an important tool for Alzheimer's disease (AD) classification from structural MRI. Many existing studies analyze individual 2D slices extracted from MRI volumes, while clinical neuroimaging practice typically relies on the full three dimensional structure of the brain. From this perspective, volumetric analysis may better capture spatial relationships among brain regions that are relevant to disease progression. Motivated by this idea, this work proposes a multimodal 3D convolutional neural network for AD classification using raw OASIS 1 MRI volumes. The model combines structural T1 information with gray matter, white matter, and cerebrospinal fluid probability maps obtained through FSL FAST segmentation in order to capture complementary neuroanatomical information. The proposed approach is evaluated on the clinically labelled OASIS 1 cohort using 5 fold subject level cross validation, achieving a mean accuracy of 72.34% plus or minus 4.66% and a ROC AUC of 0.7781 plus or minus 0.0365. GradCAM visualizations further indicate that the model focuses on anatomically meaningful regions, including the medial temporal lobe and ventricular areas that are known to be associated with Alzheimer's related structural changes. To better understand how data representation and evaluation strategies may influence reported performance, additional diagnostic experiments were conducted on a slice based version of the dataset under both slice level and subject level protocols. These observations help provide context for the volumetric results. Overall, the proposed multimodal 3D framework establishes a reproducible subject level benchmark and highlights the potential benefits of volumetric MRI analysis for Alzheimer's disease classification.

3D MRI-Based Alzheimer's Disease Classification Using Multi-Modal 3D CNN with Leakage-Aware Subject-Level Evaluation

Abstract

Deep learning has become an important tool for Alzheimer's disease (AD) classification from structural MRI. Many existing studies analyze individual 2D slices extracted from MRI volumes, while clinical neuroimaging practice typically relies on the full three dimensional structure of the brain. From this perspective, volumetric analysis may better capture spatial relationships among brain regions that are relevant to disease progression. Motivated by this idea, this work proposes a multimodal 3D convolutional neural network for AD classification using raw OASIS 1 MRI volumes. The model combines structural T1 information with gray matter, white matter, and cerebrospinal fluid probability maps obtained through FSL FAST segmentation in order to capture complementary neuroanatomical information. The proposed approach is evaluated on the clinically labelled OASIS 1 cohort using 5 fold subject level cross validation, achieving a mean accuracy of 72.34% plus or minus 4.66% and a ROC AUC of 0.7781 plus or minus 0.0365. GradCAM visualizations further indicate that the model focuses on anatomically meaningful regions, including the medial temporal lobe and ventricular areas that are known to be associated with Alzheimer's related structural changes. To better understand how data representation and evaluation strategies may influence reported performance, additional diagnostic experiments were conducted on a slice based version of the dataset under both slice level and subject level protocols. These observations help provide context for the volumetric results. Overall, the proposed multimodal 3D framework establishes a reproducible subject level benchmark and highlights the potential benefits of volumetric MRI analysis for Alzheimer's disease classification.
Paper Structure (22 sections, 6 figures, 5 tables)

This paper contains 22 sections, 6 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Research Gap
  4. Methodology
  5. Dataset Description
  6. Raw OASIS-1 Overview
  7. Labelled Subset Used in This Study
  8. Kaggle-Derived OASIS-1 Slice Dataset
  9. Problem Formulation
  10. Raw MRI Preprocessing Pipeline
  11. Supporting Diagnostic Experiments on the Slice-Based Dataset
  12. Proposed Multi-Modal 3D CNN
  13. Training and Leakage-Aware Evaluation Protocol
  14. Interpretability Analysis
  15. Results and Discussion
  16. ...and 7 more sections

Figures (6)

  • Figure 1: Overview of the proposed multi-modal 3D CNN architecture, where modality-specific encoders process T1, GM, WM, and CSF inputs independently and their learned representations are fused through a shared late-fusion classification head for binary dementia classification.
  • Figure 2: Preprocessed multi-channel MRI input. Channel 0: bias-corrected T1 image; Channels 1--3: GM, WM, and CSF probability maps from FSL FAST segmentation.
  • Figure 3: Qualitative comparison between an input MRI volume and its corresponding GradCAM visualization in the proposed multi-modal 3D framework.
  • Figure 4: Training and validation loss curves across the five subject-level cross-validation folds of the proposed multi-modal 3D CNN. The dashed vertical line in each panel marks the epoch of the selected best checkpoint for that fold.
  • Figure 5: Slice-level evaluation: input MRI slice and corresponding GradCAM map.
  • ...and 1 more figures