A Comprehensive Approach to Anticipating the Progression of Mild Cognitive Impairment.

The immersive experience provided by our approach empowers researchers with an intuitive exploration of brain structures. Within the brain's central nervous system, encompassing both white and gray matter, symptoms associated with Alzheimer's disease (AD) often manifest through gray matter decline. The manual identification of these changes proves to be a time-intensive endeavor. Although learning-based systems can detect such changes, their implementation requires substantial computational resources and extensive datasets. To surmount these challenges, we present a tailored framework designed for the categorization of distinct AD stages through brain image tissue segmentation. Our innovative approach seamlessly integrates transfer learning and fine-tuning of frozen layers and employs models such as VGG16, VGG19, AlexNet, and ResNet50. This comprehensive strategy significantly amplifies simulation outcomes across five AD categories, contributing to an overall enhancement in model efficacy. In the initial stages, our model undergoes fine-tuning to predict various AD stages, and the integration of data augmentation techniques further refines its performance. Our study culminates with the assertion that a pre-trained model, characterized by deep connectivity of dense layers, additional layers, and frozen blocks, adeptly addresses the challenges intrinsic to the proposed multiclass classification. Experimental results conclusively endorse the superior accuracy achieved through the implementation of our proposed strategy.