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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82803完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 莊曜宇(Eric Y. Chuang) | |
| dc.contributor.author | CHIA-CHUN Wu | en |
| dc.contributor.author | 吳嘉峻 | zh_TW |
| dc.date.accessioned | 2022-11-25T07:59:56Z | - |
| dc.date.copyright | 2021-11-12 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-09-10 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82803 | - |
| dc.description.abstract | "結核病（TB）是一種由結核分枝桿菌 (Mycobacterium tuberculosis) 引起的傳染性疾病，是全世界十大死亡原因之一。根據世界衛生組織（WHO）的全球結核病報告，有被診斷為結核病的人與全球預估結核病感染者人數之間存在很大差距。 痰塗片顯微鏡檢查是檢驗結核病中最廣泛使用的方法之一，因為該方法簡單、便宜、高效和快速。痰塗片顯微鏡法可以根據其染色方法分為熒光顯微鏡和明視野顯微鏡。熒光顯微鏡與明視野顯微鏡相比，其靈敏度比其高約10%並且敏感度相近。因此，它經常被用來對所有塗片做初步篩選。發光二極管熒光顯微鏡的低成本使得越來越多的國家使用它，包括一些資源有限的地區。 大量的樣本會導致專業閱片者每天需要負責遠超他們每天能負荷的抹片數量，從而降低了熒光顯微鏡篩查的敏感性。此外，還有許多人為因素會影響該方法的敏感性。 為了解決這個問題，我們開發了一個利用深度學習模型去開發了一個自動檢測結核分枝桿菌以及非結核分枝桿菌 (nontuberculous mycobacteria) 的計算機輔助檢測系統 (computer-aided detection, CAD)。我們還建立了一個圖形用戶界面提供給使用者。該CAD系統可以在70秒內完成對一張塗片的檢測，並且其擁有與人類閱片者相當甚至更高的性能表現。" | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T07:59:56Z (GMT). No. of bitstreams: 1 U0001-1009202114543700.pdf: 7814744 bytes, checksum: 28c90c48ebe0fd45d47887d0a5a9c901 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | "口試委員會審定書 # 誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF ABBREVIATIONS viii LIST OF FIGURES x LIST OF TABLES xiii Chapter 1 INTRODUCTION 1 1.1 Tuberculosis 1 1.2 TB diagnosis 1 1.3 Challenges of TB diagnosis in high TB burden areas 3 1.4 (Sputum) smear microscopy 3 1.5 Computer-aided detection system for TB diagnosis 6 1.6 Medical imaging and deep-learning-based CAD system 8 1.7 Deep learning 11 1.7.1 Fully connected layer 13 1.7.2 Convolutional layer 14 1.7.3 Pooling layer 15 1.7.4 Activation function 16 1.7.5 Loss function 17 1.7.6 Optimizer 18 1.7.7 Augmentation 19 1.8 Computer vision tasks for medical images 20 1.8.1 Image recognition 20 1.8.2 Image labeling 23 Chapter 2 MATERIALS AND METHODS 29 2.1.1 Materials 29 2.1.2 Fluorescence staining 29 2.1.3 Scanning smear slides 29 2.1.4 Objects labeling and answers of evaluation test 30 2.2 Data preprocessing 31 2.2.1 Train-valid-test split 31 2.2.2 Background-complexity-based down-sampling for negative data 32 2.2.3 Image splitting 34 2.2.4 Location-shift augmentation 36 2.2.5 Dehazing method 37 2.2.6 Single-color channel process 40 2.3 Deep neural networks 41 2.3.1 The environment of training model, evaluation, and inference 42 2.3.2 Models used for different evaluation tests 42 2.3.3 Object detection models 42 2.3.4 Image classification models 43 2.3.5 Connecting an image classifier to the object detection model 45 2.4 Graphical user interface (GUI) 46 2.5 Evaluation methods for final CAD system 47 2.5.1 Single-blind study 48 2.5.2 Double-blind study 50 2.5.3 Limit of detection (LoD) test 50 2.5.4 Recall rate test for false-negative smear slides of the human readers 51 Chapter 3 RESULTS 52 3.1 Data preprocessing evaluation 52 3.1.1 Image splitting 52 3.1.2 Dehazing method and single-color channel process 53 3.2 Deep neural network models 54 3.2.1 Object detection model 54 3.2.2 Image classification models 56 3.2.3 Connecting an image classifier to the object detection model 57 3.3 Evaluation of the final CAD system 59 3.3.1 Single-blind study 59 3.3.2 Double-blind study 61 3.3.3 Limit of detection (LoD) test 65 3.3.4 Recall rate test for false-negative smear slides of the human readers 66 3.4 Graphical user interface 67 3.4.1 Detection interface 67 3.4.2 Rechecking interface 69 3.4.3 Default setting interface 71 Chapter 4 DISCUSSION 73 4.1 Data preprocessing 73 4.1.1 Image splitting 73 4.1.2 Dehazing method and single-color channel process 73 4.2 Deep learning models 74 4.2.1 Object detection models 74 4.2.2 Image classification model 74 4.2.3 Connecting an image classifier to the object detection model 75 4.3 Evaluation of the CAD system 75 4.3.1 Single-blind study 75 4.3.2 Double-blind study 78 4.3.3 Limit of detection (LoD) test 80 4.3.4 Recall rate test for false-negative smear slides of the human readers 81 4.4 Comparison 81 4.5 Future researches 83 4.5.1 Speed of image screening 83 4.5.2 Improvement in the sensitivity of the CAD system 83 4.5.3 Improvement in the robustness of the CAD system 84 4.5.4 Advanced applications 85 Chapter 5 CONCLUSION 86 REFERENCES 87 APPENDICES 97" | |
| dc.language.iso | en | |
| dc.subject | 螢光顯微鏡檢 | zh_TW |
| dc.subject | 結核病 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 電腦輔助檢測系統 | zh_TW |
| dc.subject | tuberculosis | en |
| dc.subject | deep learning | en |
| dc.subject | computer-aided detection system | en |
| dc.subject | fluorescence microscopy | en |
| dc.title | "MTB-CAD, 利用深度學習網路建立一套針對螢光染色抹片影像、易用且高效能的電腦輔助分枝桿菌檢測系統" | zh_TW |
| dc.title | "MTB-CAD, an user-friendly, high-performance computer-aided detection system for Mycobacterium tuberculosis on fluorescence stained smear slides based on the deep learning models" | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 邱浩傑(Hsin-Tsai Liu),蔡孟勳(Chih-Yang Tseng),盧子彬,賴亮全 | |
| dc.subject.keyword | 結核病,深度學習,電腦輔助檢測系統,螢光顯微鏡檢, | zh_TW |
| dc.subject.keyword | tuberculosis,deep learning,computer-aided detection system,fluorescence microscopy, | en |
| dc.relation.page | 98 | |
| dc.identifier.doi | 10.6342/NTU202103109 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2021-09-11 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| dc.date.embargo-lift | 2024-09-17 | - |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-1009202114543700.pdf 未授權公開取用 | 7.63 MB | Adobe PDF |
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