Fuzzy Deep Learning for the Diagnosis of Alzheimer's Disease: Approaches and Challenges

Alzheimer's disease (AD) is the leading neurodegenerative disorder and primary cause of dementia. Researchers are increasingly drawn to automated diagnosis of AD using neuroimaging analyses. Conventional deep learning (DL) models excel in constructing learning classifiers in early-stage AD diagnosis. However, they often struggle with AD diagnosis due to uncertainties stemming from unclear annotations by experts, challenges in data collection, such as data harmonization issues, and limitations in equipment resolution. These factors contribute to imprecise data, hindering accurate analysis, interpretation of obtained results, and understanding of complex symptoms. In response, the integration of fuzzy logic into DL, forming fuzzy deep learning (FDL), effectively manages imprecise data and provides interpretable insights, offering a valuable advancement in AD. Therefore, exploring recent advancements in integrating DL with fuzzy logic is crucial for improving AD diagnosis. In this review, we explore the contributions of fuzzy logic within FDL models, focusing on fuzzy-based image preprocessing, segmentation, and classification. Moreover, in exploring research directions, we discuss the possibility of the fusion of multimodal data with fuzzy logic, addressing challenges in AD diagnosis. Leveraging fuzzy logic and membership while integrating diverse datasets, such as genomics, proteomics, and metabolomics may provide an effective development of a DL classifier. In addition, fuzzy explainable DL promises more accurate and linguistically interpretable decision support systems for AD diagnosis. The primary objective of this article is to serve as a comprehensive and authoritative resource for newcomers, researchers, and clinicians interested in employing FDL models for AD diagnosis.