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系統識別號 U0026-2608202018321400
論文名稱(中文) 基於深度學習技術提取超音波影像之特徵應用於腕隧道症候群診斷
論文名稱(英文) Diagnostics of Carpal Tunnel Syndrome Based on Features Extracted from Ultrasound Images by Deep Learning Technique
校院名稱 成功大學
系所名稱(中) 資訊工程學系
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 108
學期 2
出版年 109
研究生(中文) 胡凱閎
研究生(英文) Kai-Hung Hu
學號 P76074363
學位類別 碩士
語文別 英文
論文頁數 43頁
口試委員 指導教授-王士豪
口試委員-梁勝富
口試委員-吳佳慶
口試委員-廖峻德
口試委員-林奕勳
中文關鍵字 腕隧道症候群  自動分割  人工智慧分類器  腕部超音波  正中神經 
英文關鍵字 Carpal tunnel syndrome  segmentation  AI-based classifier  wrist ultrasound  median nerve 
學科別分類
中文摘要 腕隧道症候群是常見的手腕疾病,由於神經被壓迫所造成,在過去的研究中已經發現多個特徵與該疾病的相關性,如正中神經面積會因神經腫脹造成面積變大的情況、位移降低因結締組織纖維化。本研究中使用商用超音波儀器 Terason T3000 及 12L5的探頭頻率為12MHz對18位健康的受試者和21位病患進行腕部超音波影像掃描,
並自動分割超音波影像,結果為準確度70%,從影像中抽取參數,最後將參數作為輸入,使用人工智慧分類器((K-nearest neighbor、naïve Bayes、support vector machine、decision tree)進行分類,對比過去使用手動圈選的研究得到相近的準確度,而加入長寬比、堅固性參數後得到準確度的提升。分類器的結果顯示準確度在76%到85% 之間,其中KNN有最大的敏感度和特異度。本研究所提出的分類腕隧道症候群可用於輔助臨床診斷。
英文摘要 Carpal tunnel syndrome (CTS) is a kind of wrist neuropathy, which caused by elevated pressure in the carpal tunnel. In past studies, several features have been found to be related to the disease. For example, the median nerve area will be enlarged due to nerve swelling and decrease max displacement by fibrosis of connective tissue.
In this study, the commercial ultrasound instruments Terason T3000 and 12L5 were used with a probe frequency of 12MHz. 18 healthy subjects and 21 patients were scanned for wrist ultrasound images, and the ultrasound images were automatically segmented to extract parameters from the images. The result of segmentation is 70%. Finally, the parameters are used as input, and the artificial intelligence classifier (K-nearest neighbor, naïve Bayes, support vector machine, decision tree) is used for classification. Compared with the previous studies using manual labeling, the accuracy is similar, and the accuracy is improved after adding the aspect ratio and solidity parameters. The results show that the accuracy of the four classifiers is between 76% and 85%. The K-nearest neighbor has a maximum sensitivity of 90% and has a maximum specificity of 79%. The classification of carpal tunnel syndrome proposed in this study can be used to assist clinical diagnosis.
論文目次 CONTENT
摘要 I
ABSTRACT II
CONTENT III
LIST OF FIGURES V
LIST OF TABLES VII
Charpter 1 INTRODUCTION 1
1.1 Foreword 1
1.2 Research background 1
1.3 Related works 2
1.3.1 Clinical diagnosis of CTS 2
1.3.2 Ultrasound imaging analysis for CTS 4
1.3.3 Segmentation 5
1.4 Motivation and objectives 5
Charpter 2 BACKGROUND 6
2.1 Fundamental of ultrasound 6
2.1.1 Fundamental of acoustic wave 6
2.1.2 Reflection and refraction 6
2.1.3 Attenuation 7
2.1.4 Ultrasonic transducers 8
2.2 Segmentation technique 11
2.3 Classification technique 11
2.3.1 Rule-based classifier 12
2.3.2 Computational intelligence classifier 12
2.4 Anatomical structure of carpal tunnel 12
Charpter 3 MATERIALS AND METHODS 14
3.1 Segmentation 14
3.2 Classifier 15
3.2.1 K-nearest neighbor classifier 15
3.2.2 Naïve Bayes classifier 17
3.2.3 Support vector machine 18
3.2.4 Decision tree 19
3.3 Wrist measurement with ultrasound 20
Charpter 4 RESULTS AND DISCUSSION 23
4.1 Segmentation 23
4.2 Data distribution 26
4.3 Ability of classification 31
Charpter 5 Conclusions 39
5.1 Conclusions 39
5.2 Future works 39
REFERENCES 40

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