Deep learning-based Alzheimer's disease detection using magnetic resonance imaging and gene expression data

Alzheimer's disease (AD) poses significant challenges to healthcare systems across the globe. Early and accurate AD diagnosis is crucial for effective management and treatment. Recent advances in neuroimaging and genomics provide an opportunity for developing multi-modality-based AD diagnosis models using artificial intelligence (AI) techniques. However, the data complexities cause challenges in developing interpretable AI-based AD identification models. In this study, the author built a comprehensive AD diagnostic model using magnetic resonance imaging (MRI) and gene expression data. MobileNet V3 and EfficientNet B7 model was employed to extract AD features from gene expression data. The author introduced a hybrid TWIN-Performer-based feature extraction model to derive features from MRI. The attention-based feature fusion was used to fuse the crucial features. An ensemble learning-based classification model integrating CatBoost, XGBoost, and extremely randomized tree (ERT) was developed to identify cognitively normal (CN) and AD features. The proposed model was validated on diverse datasets. It achieved a superior performance on MRI and gene expression datasets. The area under the receiver operating characteristic (AUROC) scores were consistently above 0.85, indicating excellent model performance. The use of Shapley Additive exPlanations (SHAP) values improved the model's interpretability, leading to earlier interventions and personalized treatment strategies.