We will apply this methodology to the OASIS-3 and OASIS-4 datasets, which provide comprehensive longitudinal collections of structural and functional neuroimaging, clinical, and cognitive data in aging and dementia. These datasets are ideal for validating our fuzzy deep learning approach on tasks such as early detection of Alzheimer’s disease, brain tissue segmentation, and the analysis of subtle longitudinal structural changes. Model performance will be assessed against established methods, with the goal of demonstrating improvements in sensitivity, interpretability, and clinical utility.

The project is designed with reproducibility and collaboration in mind. We will release code, model architectures, and detailed documentation, enabling other researchers to replicate our findings or adapt the framework for their own studies.