Dates: 2024
Principal Investigator: Dr. Mujahed Al Dhaifallah
Description: The proposed research project aims to revolutionize Alzheimer's Disease (AD) diagnosis through deep learning and multimodal imaging. It aims to develop a comprehensive framework for early AD detection and robust diagnostic tools. Addressing current limitations, the project involves a multi-phase approach: literature review, data acquisition, transfer learning, architecture design, randomized network based classifiers and validation. Extracting significant brain tissues such as Grey Matter, White Matter, Cerebrospinal Fluid, and Leveraging pre-trained models expedites development. Also, a sophisticated multimodal architecture combining MRI and PET data enhances accuracy. Validation against clinical standards and interpretability assessment ensure reliability. Expected outputs include a literature review, curated dataset, research articles, and validated models. The project's impacts are significant, benefiting medical professionals with accurate tools and researchers with valuable resources. Bridging research and clinical practice has the potential to transform AD diagnosis, improving patient care. The methodologies employed may have broader applications in other medical fields, fostering interdisciplinary collaboration and scientific progress. Overall, this research embodies a pioneering approach to AD diagnosis, offering heightened accuracy and efficacy through advanced techniques.