A Digital and Automatic Screening System for Alzheimer’s Disease Based on Neuropsychological Test and Neural Network
碩士 === 國立臺灣大學 === 資訊工程學研究所 === 107 === Alzheimer’s disease (AD) and the other types of dementia have become one of the most serious global health issues and the fifth leading cause of death worldwide nowadays. Therefore, early detection of the disease in the stage of mild cognitive impairment (MCI),...
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| Format: | Others |
| Language: | en_US |
| Published: |
2019
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| Online Access: | http://ndltd.ncl.edu.tw/handle/66mamg |
| Summary: | 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 107 === Alzheimer’s disease (AD) and the other types of dementia have become one of the most serious global health issues and the fifth leading cause of death worldwide nowadays. Therefore, early detection of the disease in the stage of mild cognitive impairment (MCI), which is a prodromal stage of progressing to AD and mild AD, is crucial in order to improve the quality of life of the patients and to decrease the burden of their caregiver and clinicians. The aim of our study is to design a digital screening system based on the Rey-Osterrieth Complex Figure (ROCF) neuropsychological drawing test in order to assist the clinicians to detect whether the subject is MCI or AD against healthy control (HC) automatically. A data-driven deep learning approach is implemented in this work for building the screening system. An architecture of convolution neural network is designed for pre-training and extracting useful features from the figures drawn by the subjects. The learned features are then transferred to our collected dataset for further training of the classifier in order to distinguish the patients with MCI or AD against HC. As a result, a mean area under the receiver operating characteristic curve score (AUC) of 0.913 is achieved for classifying MCI vs. HC in traditional pencil and paper based ROCF called NTUH_ROCF dataset. On the other hand, dataset that collected using digitalize graphics tablet and smart pen based which is called NTUH_D-ROCF achieved 0.950 of AUC in classifying AD vs. HC.
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