運用深度學習、支持向量機及教導學習型最佳化分類糖尿病視網膜病變症狀

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姓名

葉乃寧(Nai-Ning Yeh) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

運用深度學習、支持向量機及教導學習型最佳化分類糖尿病視網膜病變症狀
(Diabetic Retinopathy Symptom Classification Using Deep Learning, SVM, and TLBO)

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摘要(中)

糖尿病視網膜病變是長期糖尿病的併發症之一，由於糖尿病視網膜病變初期症狀很少且不易察覺，因此在早期階段很難發現。現今檢測糖尿病視網膜病變的方法，需要透過醫生用特殊光學儀器，拍攝病患眼底影像，再由醫生判斷眼底有無糖尿病視網膜病變病癥。由於醫學用特殊光學儀器非常昂貴，加上醫生判斷耗時及糖尿病視網膜病變潛在病患數量多，如果有一個自動化診斷系統，能正確診斷眼底影像是否具有視網膜病變，對一般人或病患而言，搭配手持式眼底鏡，能夠達成居家照護、自我初步診斷等；對於醫生而言，可以減少診斷糖尿病視網膜病變的時間。
本論文中運用兩個獨立的深度卷積網路，產生每個病變期數的機率，搭配支持向量機得到最後的分類結果，與一般深度卷積網路不同的是，在池化層不是使用常見的2×2最大池化，而是使用局部最大池化，可以非整數倍且有效保留特徵，同時可以讓深度卷積網路的層數增加。實驗使用的資料庫，來自Kaggle其中一項競賽所提供的公開眼底資料庫，總共有將進九萬張影像，其中35124張影像當作訓練資料集，剩下的53572張影像當作測試資料集，在資料標籤部分，糖尿病視網膜病變可以分為兩種：非增殖性糖尿病視網膜病變跟增殖性糖尿病視網膜病變,其中非增殖性糖尿病視網膜病變依嚴重程度又可以分為三期，因此包含沒有罹患糖尿病視網膜病變的情況，標籤共有五類：正常眼底、初期非增殖性糖尿病視網膜病變、中期非增殖性糖尿病視網膜病變、後期非增殖性糖尿病視網膜病變及增殖性糖尿病視網膜病變。
實驗結果，在分五類的情形下，本論文所提出的方法可以達到86.17%的準確率，在只分有沒有病變的情形下，可以提升至91.05%，除了演算法設計以外，還開發了一個應用程式「Deep Retina」，透過這個應用程式，使用者可以將自己的眼底影像傳送至伺服器，伺服器會將影像輸入至訓練好的深度卷積網路，再將最終結果回傳至應用程式。

摘要(英)

Diabetic retinopathy (DR) is one of complications of long-standing diabetes, which is difficult to detect in its early stage since it only shows a few symptoms. Nowadays, the diagnosis of DR usually requires taking digital fundus images by doctors, as well as images using optical coherence tomography (OCT). Since OCT equipment is very expensive, it will benefit both the patients and the ophthalmologists if an accurate diagnosis can be made, based solely on reading digital fundus images. In the thesis, we present a novel algorithm based on deep convolutional neural network (DCNN). Unlike the traditional DCNN approach, we replace the commonly used max-pooling layers with fractional max-pooling. Two of these DCNNs with a different number of layers are trained to derive more discriminative features for classification. After combining features from meta-data of the image and DCNNs, we train a support vector machine (SVM) classifier to learn the underlying boundary of distributions of each category. For the experiments, we used the publicly available DR detection database provided by Kaggle. We used 35,124 training images and tested with 53,572 testing images. The proposed DR classifier classifies the stages of DR into five categories, labeled with an integer ranging between zero and four. The experimental results show that the proposed method can achieve a recognition rate up to 86.17%. In addition to designing a machine learning algorithm, we also develop an app called ‘Deep Retina’. Equipped with a handheld ophthalmoscope, a layman can take fundus images and perform the diagnosis automatically without intervention from ophthalmologists. It is beneficial for home care, remote medical care, and self-examination.

關鍵字(中)

★ 糖尿病視網膜病變
★ 深度學習
★ 支持向量機
★ 教導學習型最佳化

關鍵字(英)

★ Diabetic retinopathy
★ Deep learning
★ Support vector machine
★ TLBO

論文目次

Chinese Abstract ……………………………………………………... i
English Abstract ……………………………………………………... ii
Acknowledgement ……………………………………………………... iii
Table of Contents ……………………………………………………... iv
List of Figures ……………………………………………………... vi
List of Tables ……………………………………………………... viii
Chapter 1 Introduction 1

　　1.1 Research Background and Motivation …………….. 1

　　1.2 Research Purposes…………………………………. 4

Chapter 2 Literature Review 6

　　2.1 Literature Review of Retina Vessels Segmentation… 6

　　　　2.1.1 Methods of Vascular Tracking……………………… 6

　　　　2.1.2 Methods of Matched Filtering……………………… 7

　　　　2.1.3 Methods of Morphological Processing……………... 9

　　　　2.1.4 Methods of Deformation Models ………………….. 10

　　　　2.1.5 Methods of Machine Learning……………………... 11

　　2.2 Literature Review of Diabetic Retinopathy Detection…………………………………………… 12

Chapter 3 Methodology 14

　　3.1 Preprocessing………………………………………. 14

　　3.2 Fractional Max-Pooling……………………………. 17

　　3.3 Support Vector Machine…………………………… 20

　　3.4 Teaching-Learning-Based Optimization...…………. 25

　　　　3.4.1 Teacher Phase………………………………………. 26

　　　　3.4.2 Learner Phase………………………………………. 26

Chapter 4 Results and Discussion 28

　　4.1 Results……………………………………………… 28

　　4.2 Discussion………………………………………….. 34

　　　　4.2.1 Analysis of Experimental Results…………………. 34

　　　　4.2.2 Deep Learning vs. Traditional Methods 37

　　　　4.2.3 Limitations ………………………………………… 37

Chapter 5 Conclusion ………………………………………… 42

Reference ……………………………………………………... 44

參考文獻

[1] World Health Organization, Top 10 cause of death,
http://www.who.int/en/news-room/fact-sheets/detail/the-top-10-causes-of-death
[2] International Diabetes Foundation. IDF Diabetes Atlas, 8th edn. Brussels, Belgium: International Diabetes Federation, 2017.
[3] Cho N.H., Shaw J.E. Karuranga S. et al. (2018) IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045
[4] American Diabetes Association. American Diabetes Association. (2013). Eye complications.
http://www.diabetes.org/living-withdiabetes/complications/eyecomplications/?referrer=https://www.google.com.tw/
[5] Wilkinson C P, Ferris F L, Klein R E, et al. (2003) Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology. 110: 1677-1682
[6] Tufail A, Rudisill C, Egan C, et al. (2017) Automated diabetic retinopathy image assessment software: diagnostic accuracy and cost-effectiveness compared with human graders. Ophthalmology. 124: 343-351.
[7] Tufail A, Kapetanakis V V, Salas-Vega S, et al. (2016) An observational study to assess if automated diabetic retinopathy image assessment software can replace one or more steps of manual imaging grading and to determine their cost-effectiveness. Health Technol Assess. 20: xxviii-1.
[8] Liu I, Sun Y. (1993) Recursive tracking of vascular networks in angiograms based on the detection-deletion scheme. IEEE Trans. Med. Imag. 12: 334-341.
[9] Can A, Shen H, Turner J N, et al. (1999) Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms. IEEE Trans. Inf. Technol. Biomed. 3: 125-138.
[10] Vlachos M, Dermatas E. (2010) Multi-scale retinal vessel segmentation using line tracking. Computerized Medical Imaging and Graphics. 34: 213-227.
[11] Yin Y, Adel M, Bourennane S. (2012) An automatic tracking method for retinal vascular tree extraction. Proceeding of IEEE International Conference on Acoustics, Speech, and Signal Processing, Kyoto Japan (2012). IEEE.
[12] Chaudhuri S, Chatterjee S, Katz N, et al. (1989) Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imag. 8: 263-269.
[13] Hoover A, Kouznetsova V, Goldbaum M. (2000) Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imag. 19: 203-210
[14] Jiang X, Mojon D. (2003) Adaptive Local Thresholding by Verification-Based Multithreshold Probing with Application to Vessel Detection in Retinal Images. IEEE Trans. on Pattern Anal. and Mach. Intell. 25: 131-137.
[15] Zhang L, Li Q, You J, et al. (2009) A Modified Matched Filter With Double-Sided Thresholding for Screening Proliferative Diabetic Retinopathy. IEEE Trans. Inf. Technol. Biomed. 13: 528-534.
[16] Li Q, You J, Zhang D. (2012) Vessel segmentation and width estimation in retinal images using multiscale production of matched filter responses. Expert Systems with Applications. 39: 7600-7610.
[17] Zana F, Klein J C. (2001) Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans. Image Process. 10: 1010-1019.
[18] Ayala G, Leon T, Zapater V. (2005) Different Averages of a Fuzzy Set With an Application to Vessel Segmentation. IEEE Trans. Fuzzy Syst. 13: 384-393.
[19] Miri M S, Mahloojifar A. (2011) Retinal image analysis using Curvelet transform and multistructure elements morphology by reconstruction. IEEE Trans. Biomed. Eng. 58: 1183-1192.
[20] Karthika D, Marimuthu A. (2014) Retinal image analysis using contourlet transform and multistructure elements morphology by reconstruction. Proceedings of World Congress on Computing and Communication Technologies, Tiruchirappalli, India (2014). IEEE.
[21] Kass M, Witkin A, Terzopoulos D. (1988) Snake: active contour models. International Journal of Computer Vision. 1: 321-331.
[22] Espona L, Carreira M J, Ortega M, et al. (2007) A Snake for Retinal Vessel Segmentation. Proceedings of the 3rd Iberian Conference on Pattern Recognition and Image Analysis, Girona Spain (2007). Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin Germany.
[23] Al-Diri B, Hunter A. (2005) A ribbon of twins for extracting vessel boundaries. Proceedings of the 3rd European Medical and Biological Engineering Conference, Prague Czech Republic (2005).
[24] Zhang Y, Hsu W, Lee M L. (2009) Detection of retinal blood vessels based on nonlinear projections. Journal of Signal Processing Systems. 55: 103-112.
[25] Zhao Y Q, Wang X H, Wang X F, et al. (2014) Retinal vessels segmentation based on level set and region growing. Pattern Recognition. 47: 2437-2446.
[26] Roberto M. Cesar Jr, Jelinek Herbert F. (2003) Segmentation of retinal fundus vasculature in nonmydriatic camera images using wavelets. In: Angiography and Plaque Imaging. Laxminarayan S, Suri J S editors. CRC Press. 193-224.
[27] Leandro J J, Soares J V, Cesar R M, et al. (2003) Blood vessels segmentation in non-mydriatic images using wavelets and statistical classifiers. Proceedings of XVI Brazilian Symposium on Computer Graphics and Image Processing. Sao Carlos, Brazil (2003). IEEE.
[28] Ricci E, Perfetti R. (2007) Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification. IEEE Trans. Med. Imag. 26: 1357-1365.
[29] Marin D, Aquino A, Gegundez-Arias M E, et al. (2011) A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features. IEEE Trans. Med. Imag. 30: 146-158.
[30] Shanmugam V, Wahida Banu R S D. (2013) Retinal blood vessel segmentation using an Extreme Learning Machine approach. Proceedings of 2013 IEEE Point-of-Care Healthcare Technologies, Bangalore India (2013). IEEE.
[31] Wang S, Yin Y, Cao G, et al. (2015) Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing. 149: 708-717.
[32] Tolias Y A, Panas S M. (1998) A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering. IEEE Trans. Med. Imag. 17: 263-273.
[33] Xie S, Nie H. (2013) Retinal vascular image segmentation using genetic algorithm plus FCM clustering. Proceedings of 2013 Third International Conference on Intelligent System Design and Engineering Applications, Hong Kong, China (2013). IEEE.
[34] Salazar-Gonzalez A, Kaba D, Li Y, et al. (2014) Segmentation of the blood vessels and optic disk in retinal images. IEEE J. Biomed. Health Inform. 18: 1874-1886.
[35] Ege B M, Hejlesen O K, Larsen O V, et al. (2000) Screening for diabetic retinopathy using computer based image analysis and statistical classification. Computer Methods and Programs in Biomedicine. 62: 165-175.
[36] Silberman N, Ahrlich K, Fergus R, et al. (2010) Case for automated detection of diabetic retinopathy. Proceedings of AAAI Artificial Intelligence for Development, California USA (2010).
[37] Karegowda A G, Nasiha A, Jayaram M A, et al. (2011) Exudates detection in retinal images using back propagation neural network. International Journal of Computer Applications. 25: 25-31.
[38] Kavitha S, Duraiswamy K. (2011) Automatic detection of hard and soft exudates in fundus images using color histogram thresholding. European Journal of Science Research. 48: 493–504.
[39] Jorge de la Calleja, Tecuapetla L, Medina M A, et al. (2014) LBP and Machine Learning for Diabetic Retinopathy Detection. Proceedings of the 2014 Intelligent Data Engineering and Automated Learning, Salamanca Spain (2014). Springer.
[40] Fractional Max-Pooling. http://www2.warwick.ac.uk/fac/sci/statistics/staff/academic-research/graham/fmp.pdf
[41] LIBSVM: A Library for Support Vector Machines, C.-C. Chang and C.-J. Lin. http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf
[42] Rao R V, Savsani V J, Vakharia D P. (2010) Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design. 43: 303-315.
[43] Kaggle contests: Identify signs of diabetic retinopathy in eye images.
https://www.kaggle.com/c/diabetic-retinopathy-detection
[44] Christian S, Vincent V, Sergey I, et al. (2015) Rethinking the Inception Architecture for Computer Vision. (2016) IEEE Conference on Computer Vision and Pattern Recognition.
[45] Kaiming H, Xiangyu, Shaoqing R, et al. (2015) Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition.
[46] Francois C. (2016) Xception: Deep Learning with Depthwise Separable Convolutions. 2017 IEEE Conference on Computer Vision and Pattern Recognition.

指導教授

栗永徽(Yung-Hui Li)

審核日期

2018-8-16

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