Synergistic medical genetic evolutionary optimization and deep convolutional generative augmentation with SHAP-driven interpretability for precise Alzheimer's disease severity grading.

Alzheimer's disease (AD) diagnosis at an early yet accurate stage is critical to support effective treatment or intervention. Still it is not very feasible due to the presence of image data class imbalance, low interpretability of models, and a high computational cost. This research proposes a novel, end-to-end diagnostic framework that considers a Medical Genetic Algorithm (MedGA)-optimized Convolutional Neural Network (CNN) with a Deep Convolutional Generative Adversarial Network (DCGAN) to generate synthetic MRIs and SHapley Additive Explanations (SHAP) to analyse and interpret the model. The given methodology is trained and tested on the Open Access Series of Imaging Studies (OASIS) dataset. The DCGAN component introduces 700 structurally coherent synthetic images (SSIM = 0.92) into the underrepresented Moderate Dementia class, improving the overall recall by 10% and balancing the dataset. MedGA succeeds in optimizing CNN hyperparameters and resulting in complexity reduction (20%) in networks without loss of testing accuracy (97%) at the four demonstrated stages of AD: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented. SHAP analysis emphasises the role of key brain areas, the hippocampus and the amygdala in the results of classification accuracy, leading to 25% greater interpretability and clinician confidence. Comparative evaluation shows that the current framework is exceptionally better in terms of predictive performance and explainability than current state-of-the-art approaches. This combined method provides a powerful and adaptable device to categorize AD at an early age, with promising outcomes in precise diagnosis in health facilities.