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DL-EDAD Deep Learning Approach to Early Detection for Alzheimer's Disease Using E-GKFCM
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Author(s): Sanjay V. (School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India)and Swarnalatha P. (School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India)
Copyright: 2023
Pages: 15
Source title:
Deep Learning Research Applications for Natural Language Processing
Source Author(s)/Editor(s): L. Ashok Kumar (PSG College of Technology, India), Dhanaraj Karthika Renuka (PSG College of Technology, India)and S. Geetha (Vellore Institute of Technology, India)
DOI: 10.4018/978-1-6684-6001-6.ch016
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Abstract
In Alzheimer's disease (AD), memory and cognitive abilities deteriorate, affecting the capacity to do basic activities. In and around brain cells, aberrant amyloid and tau protein accumulation is believed to cause it. Amyloid deposits create plaques surrounding brain cells, whereas tau deposits form tangles inside brain cells. The plagues and tangles harm healthy brain cells, causing shrinkage. This damage seems to be occurring in the hippocampus, a brain region involved in memory formation. There are presently no methods that provide the most accurate outcomes. The current techniques do not identify AD early. The proposed DL-EDAD method achieves excellent clustering using CNN with E-GKFCM (enhanced gaussian kernel fuzzy c-means clustering). The E-GKFCM utilizes an elbow method to determine the number of clusters in a dataset. Unlike other medical pictures, brain scans are extremely sensitive.
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