<![CDATA[In recent years, there has been a substantial amount of research dedicated to using Deep Learning (DL) methods for the classification of Alzheimer's disease (AD) and other related tasks, specifically focusing on magnetic resonance imaging (MRI) data. According to a comprehensive analysis of recent studies, it seems that deep learning models, especially those that include the creation of different structures, have significant potential to improve the precision of identifying and classifying Alzheimer's disease at an early stage. This work aims to emphasize the importance of effective data preparation tactics and feature learning approaches, as well as the investigation of hybrid models using diverse deep learning technologies. This study primarily focuses on doing performance analysis of deep learning algorithms using the latest approaches. Finally, provide a concise overview and analysis of several methods that might enhance the effectiveness of identification and classification using deep learning.]]>
Copyrights © 2024
