XDementNET: An Explainable Attention Based Deep Convolutional Network to Detect Alzheimer Progression from MRI data

TL;DR

The paper tackles the challenge of early and accurate AD progression detection from MRI while ensuring interpretability. It proposes a lightweight, explainable CNN that combines multiresidual blocks with spatial and attention-based modules (group query and multi-head attention) and leverages four XAI techniques to highlight diagnostically relevant regions. The approach achieves state-of-the-art accuracies across Kaggle, OASIS, and ADNI datasets in multiple class configurations, and provides thorough explainability analyses to support clinical trust. This work has practical implications for transparent, MRI-based AD diagnostics and paves the way for multimodal or graph-informed extensions in the future.

Abstract

A common neurodegenerative disease, Alzheimer's disease requires a precise diagnosis and efficient treatment, particularly in light of escalating healthcare expenses and the expanding use of artificial intelligence in medical diagnostics. Many recent studies shows that the combination of brain Magnetic Resonance Imaging (MRI) and deep neural networks have achieved promising results for diagnosing AD. Using deep convolutional neural networks, this paper introduces a novel deep learning architecture that incorporates multiresidual blocks, specialized spatial attention blocks, grouped query attention, and multi-head attention. The study assessed the model's performance on four publicly accessible datasets and concentrated on identifying binary and multiclass issues across various categories. This paper also takes into account of the explainability of AD's progression and compared with state-of-the-art methods namely Gradient Class Activation Mapping (GradCAM), Score-CAM, Faster Score-CAM, and XGRADCAM. Our methodology consistently outperforms current approaches, achieving 99.66\% accuracy in 4-class classification, 99.63\% in 3-class classification, and 100\% in binary classification using Kaggle datasets. For Open Access Series of Imaging Studies (OASIS) datasets the accuracies are 99.92\%, 99.90\%, and 99.95\% respectively. The Alzheimer's Disease Neuroimaging Initiative-1 (ADNI-1) dataset was used for experiments in three planes (axial, sagittal, and coronal) and a combination of all planes. The study achieved accuracies of 99.08\% for axis, 99.85\% for sagittal, 99.5\% for coronal, and 99.17\% for all axis, and 97.79\% and 8.60\% respectively for ADNI-2. The network's ability to retrieve important information from MRI images is demonstrated by its excellent accuracy in categorizing AD stages.

Figures (12)

• Figure 1: An overview of the proposed method
• Figure 2: Sample images of kaggle datasets on 4 classes
• Figure 3: Sliced image of three plane of brain
• Figure 4: Architecture illustration of - (a) Multi-residual block, and (b) Custom spatial Attention block
• Figure 5: Structure of diffferent convolutional blocks' structure