A Screening System for Mild Cognitive Impairment Based on Neuropsychological Drawing Test and Neural Network

Alzheimer's disease and the other type of dementia have become one of the most serious global issues and the fifth leading cause of death worldwide nowadays. Therefore, early detection of the disease is crucial in order to improve the quality of life of the patients and to decrease the burden o...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics S. 3543 - 3548
Hauptverfasser: Cheah, Wen-Ting, Chang, Wei-Der, Hwang, Jwu-Jia, Hong, Sheng-Yi, Fu, Li-Chen, Chang, Yu-Ling
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.10.2019
Schlagworte:
Biological neural networks
Convolution
convolution neural network
Dementia
Feature extraction
Magnetic resonance imaging
Mild cognitive impairment
screening system
Training
ISSN:2577-1655
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Alzheimer's disease and the other type of dementia have become one of the most serious global issues and the fifth leading cause of death worldwide nowadays. Therefore, early detection of the disease is crucial in order to improve the quality of life of the patients and to decrease the burden of their caregiver and clinicians. Mild cognitive impairment (MCI) is a prodromal stage of progressing to Alzheimer's disease which should be focus on. In this paper, we have proposed a screening system based on the Rey-osterrieth Complex Figure, a neuropsychological test, that can automatically assist the clinicians to detect whether the subject is MCI or not. A data-driven deep learning approach is implemented in this work. Convolution autoencoder is designed initially to extract features from the input image. The features learned by the encoder are then used for further training the classifier. In order to validate the performance of our work, 59 MCI subjects and 59 healthy controls are recruited under the approval of institutional review board from the National Taiwan University Hospital. The performance of our proposed model is evaluated by using 10-fold cross-validation and it is repeated five times. As a result, a mean area under the receiver operating characteristic curve score of 0.851 and 0.810 of accuracy are achieved.
ISSN:2577-1655
DOI:10.1109/SMC.2019.8913880