| Author: | Guo, Chen |
| Title: | On the use of 3D convolutional neural network for Alzheimer's disease diagnosis |
| Advisors: | Chung, Fu-lai (COMP) |
| Degree: | M.Sc. |
| Year: | 2020 |
| Subject: | Alzheimer's disease -- Diagnosis Machine learning Medicine -- Data processing Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Computing |
| Pages: | v, 41 pages : color illustrations |
| Language: | English |
| Abstract: | Alzheimer's disease is a common chronic neurodegenerative disease. The progression of Alzheimer's disease develops slowly and become worse over time, and the patients will gradually lose the ability to live independently. Therefore, early diagnosis and interventional treatment of Alzheimer's disease can significantly delay the progression of cognitive decline and improve the living quality of patients. Currently, the computer-aided Alzheimer's disease diagnosis is emerging as a fast and efficient approach. However, training a robust neural network from scratch requires a large number of ground truth data, which is almost impossible to achieve in Alzheimer's disease diagnosis fields. Therefore, in this study, an adaptation method to improve classification accuracy on smaller datasets were tested. For this purpose, a 3D convolutional neural network was trained to classify Alzheimer's disease and normal patient using public MRI dataset. The pretrained 3D convolutional neural network achieved classification accuracy of 0.74 and 0.62 in source and target domain datasets, respectively, by using fine-tuning strategy. Then, the classification accuracy in target domain was improved from 0.62 to 0.71 by using our adaptation method, which iteratively fine-tune the model using unlabeled data. In real world scenario, medical image datasets usually consist of a small amount of ground truth data and the remaining unlabeled data. Our experimental results indicated that fine-tuning with a limited unlabeled dataset on a pre-trained model through proposed adaptation method can significantly improve classification accuracy. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 5805.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.41 MB | Adobe PDF | View/Open |
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