AGHC-neurotransformer: an attention-guided hybrid CNN-transformer model for robust classification of neurodegenerative disorders from MRI scans

Abstract

Neurodegenerative diseases like Alzheimer’s and Parkinson’s gradually damage the brain’s structure and function, leading over time to serious and lasting problems with memory, thinking, behavior, and movement. These diseases often manifest subtly in their early stages, making timely diagnosis challenging yet critical for slowing disease progression and improving patient outcomes. In our paper, we proposed an attention-guided hybrid deep learning framework for the early and accurate classification of AD, PD, and Healthy controls using axial PD-T2 weighted Magnetic Resonance Imaging (MRI) slices. Our model leverages a Convolutional Neural Network (CNN) backbone, specifically EfficientNetB0, to capture low- and mid-level spatial features with high computational efficiency.To help the model better understand the bigger picture and relationships across multiple MRI slices, we added a Transformer-based encoder after the CNN feature extractor. A Convolutional Block Attention Module (CBAM) is introduced between the CNN and Transformer components to refine feature maps by adaptively weighting spatial and channel dimensions. Furthermore, the model incorporates an attention-based slice pooling mechanism, allowing it to automatically prioritize and aggregate the most informative MRI slices from each subject without requiring manual slice annotations. The proposed hybrid architecture is trained and evaluated on a curated dataset of 2D axial brain MRI slices, which have been preprocessed and organized at the subject level. Through extensive experiments, we demonstrate that our model achieves competitive classification accuracy with a significantly lower parameter count compared to larger transfer learning baselines, such as ResNet-50 and VGG- 19. Our framework effectively combines local feature extraction, long-range dependency modeling, and adaptive attention mechanisms to address the complexities of MRI-based neurodegenerative disease diagnosis. This study states the potential of combining CNNs, Transformers, and attention modules for medical image analysis, offering a scalable, interpretable, and clinically relevant diagnostic tool for the early detection ofADand PD. Our experiments report a decent accuracy of 95% as opposed to a 93% accuracy reported by Yan’s (2025) model on our dataset, while using a significantly lower learnable parameter count. As a result, these systems could be integrated into real-life neurological screenings, thereby making it easier to identify diseases early and support early intervention and preventive strategies.