Hybrid Deep Learning Model for Hippocampal Localization in Alzheimer's Diagnosis Using U-Net and VGG16

Alzheimer's disease (AD) is a complex neurodegenerative disease that involves considerable challenges in accurately diagnosing and locating the affected brain regions. This paper proposes a new fusion model based on VGG16 and U-Net to achieve accurate segmentation of hippocampus localization and improve AD diagnostic accuracy. Compared to previous techniques such as hierarchical fully convolutional networks (FCNs) or LBP-TOP localization (an accuracy range of 68% to 95%), our approach achieved a superior accuracy (98.6%) with a mean Jaccard index of 97.3%, like the predicted accuracy range of conventional imaging analysis techniques. By utilizing pre-trained transfer learning models and sophisticated data augmentation methods, generalization to different datasets greatly reduced over-fitting. Although existing approaches usually require labor-intensive segmentation or employ handcrafted features, our model automates the hippocampus's localization, leading to improved efficiency and scalability. The effectiveness of our method is strongly supported by the performance metrics including Mean Squared Error (MSE) and Avg. error Standard Deviation which show that MSE values were 5 times lower than those produced using the Hough-CNN based approach (0.0507 vs. 4.4%). Real-world demands include the need for minimal computational complexity and dependence on pre-processed ADNI MRI datasets compromising generalizability in actual clinical frameworks. Our results demonstrated that the fusion model yields superior hippocampal segmentation performance and a new standard for AD diagnostic scores, making a substantial impact on both academic and clinical domains.