A Proposed Alzheimer's Disease Diagnosing System Based on Clustering and Segmentation Techniques | ||
| Engineering and Technology Journal | ||
| Article 12, Volume 36, 2B, December 2018, Pages 160-165 PDF (861.9 K) | ||
| DOI: 10.30684/etj.36.2B.12 | ||
| Author | ||
| Sarah J. Mohammed* | ||
| Mechanical Engineering Department, University of Technology, Baghdad, Iraq | ||
| Abstract | ||
| Alzheimer's-disease (AD) is one of the prevalent diseases that afflict the elderly. The medical field defines Alzheimer is the destruction of brain cells so that the person loses knowledge and perception, afflict both sexes and is called dementia. The medical field often suffers from accurate diagnosis and detection of the disease in the early stages. This paper presents a diagnostic approach of Alzheimer based on K-mean clustering algorithm with Markov random field segmentation on Magnetic Reasoning Images (MRI) to build software able to help the medical staff identifying and diagnosis the disease. The experimental result shows that 91% accuracy is achieved, which demonstrate the system's reliability in the medical diagnostic environment. | ||
| Keywords | ||
| Alzheimer's disease; K-mean clustering algorithm; Markov random field | ||
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