Explainable AD classification using integrated quantum-inspired deep neural and transformer models

The classification of Alzheimer’s disease (AD) is often not consistent and reliable due to heterogeneous neurodegenerative patterns and the non-generalization of various imaging sources. This study proposes an explainable and robust classification framework that will be able to retain its performance under in-distribution and blind-testing settings. The study uses 4 publicly available brain MRI datasets covering several stages of AD including non-demented, mild cognitive impairment and progressive Alzheimer’s stages. The datasets consist of balanced and augmented scans, and clinically heterogeneous scans were acquired using different imaging and acquisition techniques. A dedicated independent dataset is used for blind evaluation to check robustness against unseen-source domain shifts. An explainable hybrid framework, named Tri-Fusion-ADNet, is proposed in this work. It consists of EfficientNet-V2-S for localized neuro-anatomical feature extraction, Swin TransformerV2 for modelling long-range contextual dependencies and a quantum-inspired variational neural network (QI-VNN) for enhanced interaction of higher-order features. Before the classification, fusion has been done on feature levels. The reliability of the model is checked through 10-fold cross-validation on various datasets. Further, qualitative interpretability has been supported through visualization using Grad-CAM.The proposed Tri-Fusion-ADNet performed well in all data sets. The testing accuracy of the model was 98.42% (MCC = 0.979) on the balanced augmented dataset (D-I). The accuracy was 97.86% (MCC = 0.973) on augmented dataset (D-II). The clinically heterogeneous dataset (D-III) gave 96.21% (MCC = 0.949). Also, 91.18 when trained on D-I and tested on D-II, and 91.64 when trained on D-II and tested on D-I, cross-dataset testing without adaptation yields very high accuracies. In addition, during blind cross-dataset evaluation on an independent unseen dataset (D-IV), the framework maintained a robust accuracy of 91.22%, validating its generalization ability of domain shift.