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dc.contributor.authorTung-Sheng Hsiehen_US
dc.identifier.citation[1] C. Cortes and V. Vapnik, 'Support-Vector Networks', Kluwer Academic Publishers, Boston, Manufactured in The Netherlands, 1995. [2] N. S. Altman, 'An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression', The American Statistician, vol. 46, no. 3, pp. 175-185, 1992. [3] C. M. Bishop, 'Neural Networks for Pattern Recognition', Oxford University Press, Inc. New York, NY, USA, 1995. [4] M. T. Hagan, H. B. Demuth, and M. H. Beale, 'Neural Network Design', PWS Publishing Company, 1996. [5] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Habbard, and L. D. Jackel, 'Handwritten Digit Recognition with a Back-Propagation Network', Advances in neural information processing systems 2, pp. 396-404, 1990. [6] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, 'Backpropagation Applied to Handwritten Zip Code Recognition', Neural Computation, vol. 1, no. 4, pp. 541-551, 1989. [7] F. Schroff., D. Kalenichenko, and J. Philbin, 'FaceNet: A unified embedding for face recognition and clustering', IEEE Conference on Computer Vision and Pattern Recognition, pp. 815-823, 2015. [8] Y. Taigman, M. Yang, M. A. Ranzato, and L. Wolf, 'DeepFace: Closing the Gap to Human-Level Performance in Face Verification', IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701-1708, 2014. [9] A. Krizhevsky, I. Sutskever, and G. E. Hinton, 'ImageNet classification with deep convolutional neural networks', 25th International Conference on Neural Information Processing Systems, vol. 1, pp. 1097-1105, 2012. [10] S. Ren, K. He, R. Girshick, and J. Sun, 'Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks', 28th International Conference on Neural Information Processing Systems, vol. 1, pp. 91-99, 2015. [11] R. Girshick, 'Fast R-CNN', IEEE International Conference on Computer Vision, pp. 1440-1448, 2015. [12] R. Girshick, J. Donahue, T. Darrell, and J. Malik, 'Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation', IEEE Conference on Computer Vision and Pattern Recognition, pp. 580-587, 2014. [13] M. Leach, M. Drummond, and A. Doig, 'Practical Flow Cytometry in Haematology Diagnosis', Wiley-Blackwell, 2013. [14] B. J. Bain, 'Blood Cells: A Practical Guide, 5th Edition', Wiley-Blackwell, 2015. [15] R. Kohavi, 'A study of cross-validation and bootstrap for accuracy estimation and model selection', 14th international joint conference on Artificial intelligence, vol. 2, pp. 1137-1143, 1995. [16] S. Arlot and A. Celisse, 'A survey of cross-validation procedures for model selection', Statistics Surveys, vol. 4, pp. 40-79, 2010. [17] T. Ahonen, A. Hadid, and M. Pietikäinen, 'Face Recognition with Local Binary Patterns', European Conference on Computer Vision, pp. 469-481, 2004. [18] N. Dalal and B. Triggs, 'Histograms of Oriented Gradients for Human Detection', IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886-893, 2005. [19] R. C. Gonzalez and R. E. Woods, 'Digital Image Processing', Prentice Hall, 2002. [20] R. C. Gonzalez, R. E. Woods, and S. L. Eddins, 'Digital Image Processing Using MATLAB', Pearson Prentice Hall, 2013.zh_TW
dc.description.abstract血液抹片檢查在臨床上的用途有許多種,包含了血球分類計數、骨髓檢查等。我們可以從血片中得知病人的血液狀況,以及有助於醫生診斷病情及了解治療效果。然而,這些工作是會耗費許多時間與人力的。所以本文所做的研究希望可以在白血球檢查這方面幫助到他們。 近年來,深度學習在機器學習中變得熱門,其中包含了捲積神經網路。捲積神經網路在影像辨識上的效果良好,本文以此方法來偵測與分類白血球細胞。我們使用了自己從台中慈濟醫院取得的資料庫來做實驗。為了能夠找出最有效率的網路,我們嘗試了各種不同的神經網路架構。除此之外,我們使用了Faster R-CNN來幫助我們偵測白血球細胞在血片影像上的位置。另外,我們也使用了Holdout驗證方法來確保我們實驗結果的可信度,而我們的辨識率能達到超過99%。zh_TW
dc.description.abstractBlood film examination has many kinds of clinical application, including blood cell classification, blood cell count, and bone marrow examination. From the blood film, we can know the patient's blood condition. In addition, it can help doctors to diagnose the patient's condition and understand the effect of treatment. However, these jobs take a lot of time and labor. Therefore, we hope that the research in this thesis can help them for the white blood cell (WBC) examination. In recent years, deep learning has become popular in machine learning, including the convolutional neural networks (CNN). Convolutional neural networks have good performances on image recognition. In this thesis, we use this method to detect and classify white blood cells. The experiments were conducted by using the dataset that was taken from Taichung Tzu Chi Hospital. In order to find the most effective neural network, we have tried many different neural networks. In addition, we use the Faster R-CNN to help us to detect the white blood cells on the blood film image. Moreover, we also use the Holdout validation method to ensure the credibility of the experimental results, and the probability of the recognition can reach more than 99%.en_US
dc.description.tableofcontents誌謝 i 中文摘要 ii Abstract iii List of Figures v List of Tables vii CHAPTER 1 Introduction 1 CHAPTER 2 Background 3 2.1 Neural Network to deep learning 3 2.2 Convolutional Neural Networks (CNN) 4 2.2.1 Convolution layer 5 2.2.2 Max pooling layer 6 2.2.3 Fully connected layer 7 2.3 Faster R-CNN 8 CHAPTER 3 Experimental materials 12 3.1 Data set 12 3.2 Training and testing data of CNN 13 3.3 Training data of Faster R-CNN 14 3.4 Network architecture of CNN 15 3.5 Network architecture of Faster R-CNN 15 CHAPTER 4 Experimental process 16 4.1 Detection part 16 4.2 Classification part 17 4.3 Holdout method for data validation 18 CHAPTER 5 Experimental results 19 5.1 Detection cases 19 5.2 Classification cases 23 5.3 Holdout method for the classification results 28 5.4 Comparison with previous classification methods 29 CHAPTER 6 Conclusion 30 References 31zh_TW
dc.subjectFaster R-CNNzh_TW
dc.subjectConvolutional neural networks (CNN)en_US
dc.subjectwhite blood cell (WBC)en_US
dc.subjectimage recognitionen_US
dc.subjectFaster R-CNNen_US
dc.titleDetection and classification of white blood cells using convolutional neural networks (CNN)en_US
dc.typethesis and dissertationen_US
item.openairetypethesis and dissertation-
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