Abstract

AD is a progressive neurodegenerative disorder impacting specific brain sub-regions. Accurate identification and analysis of these regions are crucial for early diagnosis and effective intervention. This study employs optimization techniques to enhance the understanding of AD-related alterations in brain sub-regions. Utilizing medical imaging data, a multi-step approach is implemented. Image segmentation algorithms optimize brain sub-region delineation, while feature selection techniques enhance discriminative information extraction. Machine learning models, fine-tuned through optimization, classify images into AD and non-AD categories. Functional connectivity patterns between sub-regions are explored using network optimization methods. Predictive modeling and treatment planning incorporate optimization for improved accuracy and personalized strategies. This research contributes a comprehensive framework for analyzing AD-affected brain sub-regions, integrating optimization techniques into various stages of analysis. The proposed approach enhances diagnostic accuracy, provides insights into disease mechanisms, and facilitates personalized treatment strategies. The optimized methods demonstrate superior accuracy in image segmentation, classification, and predictive modeling. Connectivity analysis reveals significant alterations, offering novel insights. Personalized treatment plans, optimized for individual patients, show promise in improving therapeutic outcomes.