A Fuzzy Choquet Integral–Based Hybrid Deep Learning Framework for Robust Multiclass Alzheimer’s Detection

Abstract

Reliable multiclass staging of Alzheimer’s disease from brain MRI remains challenging because adjacent disease stages often present subtle, heterogeneous, and visually overlapping structural patterns. Single deep-learning backbones may therefore produce unstable predictions, especially under class imbalance and borderline stage separation. To address this limitation, this study proposes FCI-ResNet, an interaction-aware fuzzy decision-fusion framework for four-class Alzheimer’s MRI classification. The framework integrates three ImageNet-pretrained ResNetV2 backbones, ResNet50V2, ResNet101V2, and ResNet152V2, as complementary feature learners and fuses their class-probability outputs using the fuzzy Choquet integral (FCI). Unlike voting, averaging, or distance-based fusion, the proposed FCI strategy models both individual learner reliability and interlearner dependency, enabling more principled decision fusion for uncertain multiclass predictions. A controlled experimental protocol was adopted on the Mendeley Alzheimer MRI dataset, including dataset verification, split-before-preprocessing, ROI-focused preprocessing, train-only augmentation, validation-only FCI optimization, isolated test-set evaluation, and class-imbalance–aware assessment. On the Mendeley test set, FCI-ResNet achieved 97.81% accuracy, 97.82% precision, 97.81% recall, 97.81% F1-score, 96.93% macro F1-score, 96.40% MCC, and 99.86% AUC, outperforming individual ResNetV2 backbones and the FHD comparator. External validation on the independent OASIS dataset further demonstrated strong generalization, achieving 99.31% accuracy, 99.47% precision, 99.47% recall, 99.31% F1-score, 99.47% macro F1-score, 99.00% MCC, and 99.98% AUC. Bootstrap confidence intervals, McNemar significance testing, discordant-pairs analysis, ablation study, confusion matrix evaluation, computational complexity analysis, and Grad-CAM visualization collectively support the robustness, reliability, interpretability, and practical relevance of the proposed framework. Overall, FCI-ResNet provides an externally validated and statistically supported MRI-based decision-fusion framework for robust multiclass Alzheimer’s disease staging.