Early diagnosis of Alzheimer’s Disease using hybrid CNN-Transformer models with Grad-CAM interpretability

Detecting Alzheimer’s Disease (AD) at an early stage is vital because it enables prompt treatment and intervention, which can help slow disease progression and enhance patient prognosis. Given the increasing prevalence of AD globally, with an estimated 50 million people currently living with the condition and projected to triple by 2050, the development of accurate and efficient diagnostic tools is paramount. In this study, a novel architecture for the early diagnosis of AD by combining Convolutional Neural Networks (CNNs) or Vision Transformers (ViTs) with traditional Machine Learning (ML) algorithms was proposed. Utilizing MRI images as input, CNNs/ViTs serve as feature extractors, while demographic data is integrated to enhance diagnostic accuracy. Through extensive experimentation, our proposed model, which utilizes a CNN backbone optimized for MRI analysis as a feature extractor and LGBM as the classifier, achieved superior accuracy, reaching up to 96.83%. Statistical validation through confidence intervals and McNemar’s test further demonstrated the robustness and significant performance improvements of the proposed model compared to baseline methods. This study employs eXplainable AI techniques to visualize critical regions in MRI images that influence the model’s diagnostic decisions, promoting clinical transparency and trust in AI-assisted early diagnosis of AD. The novelty of this study lies in integrating deep feature extractors (CNNs/ViTs) with traditional ML classifiers, supported by interpretability through Grad-CAM and statistical validation, offering a transparent and accurate framework for early diagnosis of AD.