A Novel Prognostic Model for the Earlier Diagnostic of Alzheimer's Disease Fusing Fuzzy Logic and Capsule Networks

Alzheimer's disease (AD) is a widespread neurodegenerative diseases and primary cause of dementia whose pathology results in memory loss and other cognitive deficits. It therefore calls for rapid and correct identification so as to manage the disease effectively. The recent research on deep learning (DL) techniques points to the usefulness of reading magnetic resonance images (MRIs) in finding AD and forecasting its spreading trends. Nonetheless, traditional deep learning models have unavoidable performance limitations such as data uncertainty, harmonization obstacles, and resolution inadequacies of the types of physical equipment used. In this study, a novel Alzheimer's disease (AD) diagnosis system that combines fuzzy logic and capsule networks is proposed to support therapy at an early stage. This model solves the problem as they take advantage of fuzzy logic features of the capsule network while containing erasures of image feature spatial location that are important for precise image feature classification. This is emphasized whereby the fuzzy logic enhances the interpretability of the model by constraining any subjectivity present within the neuroimaging data, while capsule networks also allow for enough feature extraction and classification. This union improves stage-wise categorization and the ability to spot minute changes in normal brain MRI images, making it an easier diagnosis and understanding tool for the physicians. The proposed model tackles the problem of early AD diagnosis to the effect that by addressing the effect of AD on elderly patients, predictive insights are made better and more interpretable than they would be without the model.