A Meta-Heuristic Multi-Objective Optimization Method for Alzheimer’s Disease Detection Based on Multi-Modal Data

Alzheimer’s disease (AD) is a neurodegenerative that affects large number of people across the globe. Even though AD one most commonly seen brain disorders, it difficult to detect and requires categorical representation features differentiate similar patterns. Research into more complex problems, such as detection, frequently employs neural networks. Those approaches are regarded well-understood even sufficient by researchers scientists without formal training in artificial intelligence. Thus, imperative identify method detection fully automated user-friendly non-AI experts. The should find efficient values for models’ design parameters promptly simplify network process subsequently democratize Further, multi-modal medical image fusion has richer modal superior ability represent information. A formed integrating relevant complementary information from multiple input images facilitate accurate diagnosis better treatment. This study presents MultiAz-Net novel optimized ensemble-based deep learning model incorporate heterogeneous PET MRI diagnose disease. Based on extracted fused data, we propose an procedure predicting onset at early stage. Three steps involved proposed architecture: fusion, feature extraction, classification. Additionally, Multi-Objective Grasshopper Optimization Algorithm (MOGOA) presented multi-objective optimization algorithm optimize layers MultiAz-Net. desired objective functions imposed achieve this, searched corresponding values. ensemble been tested perform four categorization tasks, three binary categorizations, multi-class task utilizing publicly available Alzheimer neuroimaging dataset. achieved (92.3 ± 5.45)% accuracy multi-class-classification task, significantly than other models have reported.