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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 王昭男(Chao-Nan Wang) | |
dc.contributor.author | Yu-Hsuan Chiu | en |
dc.contributor.author | 邱昱軒 | zh_TW |
dc.date.accessioned | 2021-06-07T17:58:43Z | - |
dc.date.copyright | 2020-08-11 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-07-31 | |
dc.identifier.citation | [1] T. M. Daniel, 'The history of tuberculosis,' Respiratory medicine, vol. 100, no. 11, pp. 1862-1870, 2006. [2] World Health Organization, Global tuberculosis report 2018. World Health Organization, 2018. [3] World Health Organization, The End TB Strategy. World Health Organization, 2015. [4] Centers for Disease Control and Prevention, 'Core Curriculum on Tuberculosis: What the Clinician Should Know,' 2014. [5] F. Rahman, S. K. Munshi, S. M. Kamal, A. M. Rahman, M. M. Rahman, and R. Noor, 'Comparison of different microscopic methods with conventional TB culture,' Stamford Journal of Microbiology, vol. 1, no. 1, pp. 46-50, 2011. [6] Tung-Ti Liu, 'Dsign and Investigation on Identification of Tubercle Bacilli Image System,' Master, 電機與控制工程系所, National Chiao Tung University, Hsinchu City, 2007. [7] P. Sadaphal, J. Rao, G. Comstock, and M. Beg, 'Image processing techniques for identifying Mycobacterium tuberculosis in Ziehl-Neelsen stains,' The International Journal of Tuberculosis and Lung Disease, vol. 12, no. 5, pp. 579-582, 2008. [8] Zhi-Han Chen, 'Automatic Mycobacterium Tuberculosis Identification System,' Master, 資訊工程學系碩博士班, National Cheng Kung Univeristy, Tainan City, 2013. [9] 林展頤, '應用自動彩色顯微影像分割之結核菌偵測與評估,' 碩士, 資訊工程學系碩博士班, 國立成功大學, 台南市, 2009. [10] C. F. F. Costa Filho, P. C. Levy, C. d. M. Xavier, L. B. M. Fujimoto, and M. G. F. J. R. o. B. E. Costa, 'Automatic identification of tuberculosis mycobacterium,' vol. 31, no. 1, pp. 33-43, 2015. [11] P. Rajpurkar et al., 'Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning,' arXiv preprint arXiv:1711.05225, 2017. [12] S. Hwang, H.-E. Kim, J. Jeong, and H.-J. Kim, 'A novel approach for tuberculosis screening based on deep convolutional neural networks,' in Medical Imaging 2016: Computer-Aided Diagnosis, 2016, vol. 9785: International Society for Optics and Photonics, p. 97852W. [13] P. Lakhani and B. Sundaram, 'Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks,' Radiology, vol. 284, no. 2, pp. 574-582, 2017. [14] S. Kant and M. M. J. a. p. a. Srivastava, 'Towards Automated Tuberculosis detection using Deep Learning,' 2018. [15] R. O. Panicker, K. S. Kalmady, J. Rajan, and M. K. Sabu, 'Automatic detection of tuberculosis bacilli from microscopic sputum smear images using deep learning methods,' Biocybernetics and Biomedical Engineering, vol. 38, no. 3, pp. 691-699, 2018. [16] Y. Xiong, X. Ba, A. Hou, K. Zhang, L. Chen, and T. Li, 'Automatic detection of mycobacterium tuberculosis using artificial intelligence,' J Thorac Dis, vol. 10, no. 3, pp. 1936-1940, Mar 2018. [17] S. Lopez-Garnier, P. Sheen, and M. Zimic, 'Automatic diagnostics of tuberculosis using convolutional neural networks analysis of MODS digital images,' PloS one, vol. 14, no. 2, 2019. [18] Y. LeCun et al., 'Backpropagation applied to handwritten zip code recognition,' Neural computation, vol. 1, no. 4, pp. 541-551, 1989. [19] Y. LeCun, 'LeNet-5, convolutional neural networks,' vol. 20, 2015. [20] K. Simonyan and A. Zisserman, 'Very deep convolutional networks for large-scale image recognition,' arXiv preprint arXiv:1409.1556, 2014. [21] J. Redmon and A. Farhadi, 'YOLO9000: better, faster, stronger,' in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 7263-7271. [22] G. Litjens et al., 'A survey on deep learning in medical image analysis,' Medical image analysis, vol. 42, pp. 60-88, 2017. [23] W. Jifara, F. Jiang, S. Rho, M. Cheng, and S. Liu, 'Medical image denoising using convolutional neural network: a residual learning approach,' The Journal of Supercomputing, vol. 75, no. 2, pp. 704-718, 2019. [24] Q. Li, W. Cai, X. Wang, Y. Zhou, D. D. Feng, and M. Chen, 'Medical image classification with convolutional neural network,' in 2014 13th International Conference on Control Automation Robotics Vision (ICARCV), 2014: IEEE, pp. 844-848. [25] Y. Bar, I. Diamant, L. Wolf, S. Lieberman, E. Konen, and H. Greenspan, 'Chest pathology detection using deep learning with non-medical training,' in 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 2015: IEEE, pp. 294-297. [26] B. Q. Huynh, H. Li, and M. L. Giger, 'Digital mammographic tumor classification using transfer learning from deep convolutional neural networks,' Journal of Medical Imaging, vol. 3, no. 3, p. 034501, 2016. [27] X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R. M. Summers, 'Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases,' in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2097-2106. [28] O. Ronneberger, P. Fischer, and T. Brox, 'U-Net: Convolutional Networks for Biomedical Image Segmentation,' Cham, 2015: Springer International Publishing, pp. 234-241. [29] F. Milletari, N. Navab, and S.-A. Ahmadi, 'V-net: Fully convolutional neural networks for volumetric medical image segmentation,' in 2016 Fourth International Conference on 3D Vision (3DV), 2016: IEEE, pp. 565-571. [30] S. J. Pan and Q. Yang, 'A survey on transfer learning,' IEEE Transactions on knowledge and data engineering, vol. 22, no. 10, pp. 1345-1359, 2009. [31] H.-C. Shin et al., 'Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning,' IEEE transactions on medical imaging, vol. 35, no. 5, pp. 1285-1298, 2016. [32] A. Van Opbroek, M. A. Ikram, M. W. Vernooij, and M. De Bruijne, 'Transfer learning improves supervised image segmentation across imaging protocols,' IEEE transactions on medical imaging, vol. 34, no. 5, pp. 1018-1030, 2014. [33] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, 'SMOTE: synthetic minority over-sampling technique,' Journal of artificial intelligence research, vol. 16, pp. 321-357, 2002. [34] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, 'Image quality assessment: from error visibility to structural similarity,' IEEE transactions on image processing, vol. 13, no. 4, pp. 600-612, 2004. [35] Z. C. Lipton, 'The mythos of model interpretability,' Queue, vol. 16, no. 3, pp. 31-57, 2018. [36] W. P. Lord and D. C. Wiggins, 'Medical decision support systems,' in Advances in Health care Technology Care Shaping the Future of Medical: Springer, 2006, pp. 403-419. [37] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, 'Grad-cam: Visual explanations from deep networks via gradient-based localization,' in Proceedings of the IEEE international conference on computer vision, 2017, pp. 618-626. [38] J. Irvin et al., 'Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison,' in Proceedings of the AAAI Conference on Artificial Intelligence, 2019, vol. 33, pp. 590-597. [39] B. Sahiner et al., 'Deep learning in medical imaging and radiation therapy,' Medical physics, vol. 46, no. 1, pp. e1-e36, 2019. [40] S. S. Han, M. S. Kim, W. Lim, G. H. Park, I. Park, and S. E. Chang, 'Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm,' Journal of Investigative Dermatology, vol. 138, no. 7, pp. 1529-1538, 2018. [41] A. Tiulpin, J. Thevenot, E. Rahtu, P. Lehenkari, and S. Saarakkala, 'Automatic knee osteoarthritis diagnosis from plain radiographs: a deep learning-based approach,' Scientific reports, vol. 8, no. 1, pp. 1-10, 2018. [42] L. Li et al., 'Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT,' Radiology, p. 200905, 2020. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/16033 | - |
dc.description.abstract | 肺結核目前是全球第十大死因,嚴重威脅了全人類,尤其是發展中國家的公共衛生。為了能夠及早診斷治療肺結核,自動化電腦輔助診斷系統變得十分重要。本文提出了一個基於卷積式神經網路及遷移學習的電腦輔助診斷系統,用以分類肺結核檢驗之黃金標準—痰液培養檢測—的培養結果。結合遷移學習以及本文提出的資料切割方法,我們克服了小且不平衡的醫療資料集為卷積式神經網路訓練過程所帶來的挑戰。另外,由於「非陰性類別」的表現在醫療檢測領域上十分重要,我們設計了一個兩階段分類方法來改善「非陰性類別」的分類結果。我們以來自台灣桃園醫院的16,503張真實痰液培養影像來驗證本研究的貢獻。實驗結果顯示,本文提出的系統在非陰性類別達到了98%的召回率以及99%的精準率。 (因本研究正商談技轉中,故以CONFIDENTIAL閉門口試進行。) | zh_TW |
dc.description.abstract | Tuberculosis (TB) is the tenth cause of death in the world. It has seriously threatened human health, especially in developing countries. A computer-aided diagnosis (CADx) system is necessary to accelerate the TB diagnosis for early treatment. In this thesis, we propose a CADx system using convolutional neural network and transfer learning to classify the result of “TB culture test” - the gold standard for TB diagnostic test. We apply transfer learning and propose a data splitting method to resolve the challenge of the small and imbalanced dataset. In addition, as the performance of non-negative class is crucial in this application, we introduce a two-stage classification method (TSCM) to boost the results. Experiments use a real clinical dataset of TB culture test (16,503 images from Tao-Yuan General Hospital, Taiwan) to verify our work. Our proposed method achieves 98% recall and 99% precision on non-negative class. (This thesis is CONFIDENTIAL with a defence behind closed doors as its technical transfer is still under discussion.) | en |
dc.description.provenance | Made available in DSpace on 2021-06-07T17:58:43Z (GMT). No. of bitstreams: 1 U0001-3107202012432600.pdf: 3039738 bytes, checksum: 7cd96f460cc0c60a4aa10794f041ff67 (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES ix Chapter 1 Introduction 1 Chapter 2 Related Works 7 2.1 CADx Systems of TB 7 2.2 CNN 8 2.3 Transfer Learning 12 2.4 Handling Data Imbalance 13 Chapter 3 Dataset 16 Chapter 4 Proposed Method 19 4.1 Problem Objective and Metric 19 4.2 Baseline Model 20 4.3 Determining Preserve Ratio 21 4.4 Deciding Data Balancing Method 22 4.5 Direct Deep Learning Classification 24 4.5.1 CNN 25 4.5.2 VGG 25 4.5.3 YOLO 27 4.6 TSCM 27 4.7 Experiment Settings 29 Chapter 5 Results and Discussion 31 5.1 Evaluation of Preserved Ratio 31 5.2 Evaluation of Data Balancing Methods 34 5.3 Performance of Direct Deep Learning Classification 37 5.4 Efficacy of TSCM 42 5.5 Overall Discussion 46 5.6 Visual Explanations of TS-YOLO 49 Chapter 6 Conclusion 53 REFERENCES 55 | |
dc.language.iso | en | |
dc.title | 基於卷積神經網路與遷移學習的兩階段結核菌培養檢測 | zh_TW |
dc.title | Two-stage Tuberculosis Culture Diagnosis based on Convolutional Neural Network and Transfer Learning | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 張瑞益(Ray-I Chang) | |
dc.contributor.oralexamcommittee | 張恆華(Herng-Hua Chang),丁肇隆(Chao-Lung Ting) | |
dc.subject.keyword | 自動肺結核檢測,肺結核痰液培養檢驗,遷移式學習,卷積式神經網路,兩階段分類方法, | zh_TW |
dc.subject.keyword | Automatic tuberculosis diagnosis,Tuberculosis culture test,Transfer learning,Convolutional neural network,Two-stage classification, | en |
dc.relation.page | 58 | |
dc.identifier.doi | 10.6342/NTU202002151 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2020-08-02 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
顯示於系所單位: | 工程科學及海洋工程學系 |
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