Unlocking the Potential of XAI for Improved Alzheimer’s Disease Detection and Classification Using a ViT-GRU Model

• Published in: IEEE access 2024, Vol.12, p.8390-8412
• Main Authors: Mahim, S. M., Ali, Md. Shahin, Hasan, Md. Olid, Nafi, Abdullah Al Nomaan, Sadat, Arefin, Hasan, Shakib Al, Shareef, Bryar, Ahsan, Md. Manjurul, Islam, Md. Khairul, Miah, Md. Sipon, Niu, Ming-Bo
• Format: Article
• Language: English
• Subjects: Accuracy; Alzheimer’s disease; attention map; Classification; Datasets; deep learning; Diagnosis; Explainable artificial intelligence; Machine learning; Magnetic resonance imaging; Medical imaging; ViT-GRU; XAI
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3351809

Alzheimer’s Disease (AD) is a significant cause of dementia worldwide, and its progression from mild to severe affects an individual’s ability to perform daily activities independently. The accurate and early diagnosis of AD is crucial for effective clinical intervention. However, interpreting AD from medical images can be challenging, even for experienced radiologists. Therefore, there is a need for an automatic diagnosis of AD, and researchers have investigated the potential of utilizing Artificial Intelligence (AI) techniques, particularly deep learning models, to address this challenge. This study proposes a framework that combines a Vision Transformer (ViT) and a Gated Recurrent Unit (GRU) to detect AD characteristics from Magnetic Resonance Imaging (MRI) images accurately and reliably. The ViT identifies crucial features from the input image, and the GRU establishes clear correlations between these features. The proposed model overcomes the class imbalance issue in the MRI image dataset and achieves superior accuracy and performance compared to existing methods. The model was trained on the Alzheimer’s MRI Preprocessed Dataset obtained from Kaggle, achieving notable accuracies of 99.53% for 4-class and 99.69% for binary classification. It also demonstrated a high accuracy of 99.26% for 3-class on the AD Neuroimaging Initiative (ADNI) Baseline Database. These results were validated through a thorough 10-fold cross-validation process. Furthermore, Explainable AI (XAI) techniques were incorporated to make the model interpretable and explainable. This allows clinicians to understand the model’s decision-making process and gain insights into the underlying factors driving the AD diagnosis.