An Intelligent Detection of Early Neurocognitive Disorders Using Deep Learning Approach
TL;DR
This method expedites the process of generating trustworthy results by evaluating data simultaneously, which lowers the number of biomarkers required and related expenses for precise patient diagnoses, and has potential in therapeutic contexts by enabling accurate and customized detection of AD.
Abstract
: Alzheimer's disease (AD) is the most common cause of dementia worldwide, and its incidence is rising. Although it usually affects those over 60, middle-aged people are more affected. However, the approval process for new drugs has been significantly delayed for more than ten years. The complex pathophysiology of AD presents obstacles for immunotherapy, despite its potential benefits. Existing Convolutional Neural Network (CNN) methods have drawbacks, regardless of whether they use region-or vertex-level input. While region-wise techniques find capturing minute spatial differences in cortical thickness difficult, vertex-wise methods are typically noisy. Using a Region with Convolutional Neural Network (RCNN) for more efficient feature extraction and classification is one suggested solution to these problems. This method expedites the process of generating trustworthy results by evaluating data simultaneously, which lowers the number of biomarkers required and related expenses for precise patient diagnoses. As such, it has potential in therapeutic contexts by enabling accurate and customized detection of AD.