Authors : DhirajKumar Gupta , Bhumishree Patil, Kalyani Fender, Atharva Rajkondawar
DOI : 10.46335/IJIES.2024.9.6.3
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Abstract—This article provides a comprehensive re- view of the use of deep learning techniques in the study of neurodegenerative disease detection and high- lights their importance in addressing the emerging health challenges of these diseases. The study inves- tigates the creation and implementation of a com- plex deep learning model within an accessible web application, with the potential to reshape diagnostic practices. Through the utilization of cutting-edge ma- chine learning methodologies, the model demonstrates the capability for accurate and early identification of Alzheimer’s disease, thereby enabling personalized treatment strategies and forecasting of disease progres- sion. These advancements not only improve patient well-being but also contribute to a more profound understanding of the disease’s trajectory and potential treatment options. Finally, this review highlights the importance of ongoing research on deep learning in the study of neurodegenerative disease detection and their potential to impact patient outcomes..
