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Detection and classification of white blood cells using convolutional neural networks (CNN)
|關鍵字:||捲積神經網路;白血球;影像辨識;Faster R-CNN;Convolutional neural networks (CNN);white blood cell (WBC);image recognition;Faster R-CNN||引用:|| C. Cortes and V. Vapnik, 'Support-Vector Networks', Kluwer Academic Publishers, Boston, Manufactured in The Netherlands, 1995.  N. S. Altman, 'An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression', The American Statistician, vol. 46, no. 3, pp. 175-185, 1992.  C. M. Bishop, 'Neural Networks for Pattern Recognition', Oxford University Press, Inc. New York, NY, USA, 1995.  M. T. Hagan, H. B. Demuth, and M. H. Beale, 'Neural Network Design', PWS Publishing Company, 1996.  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.  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.  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.  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.  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.  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.  R. Girshick, 'Fast R-CNN', IEEE International Conference on Computer Vision, pp. 1440-1448, 2015.  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.  M. Leach, M. Drummond, and A. Doig, 'Practical Flow Cytometry in Haematology Diagnosis', Wiley-Blackwell, 2013.  B. J. Bain, 'Blood Cells: A Practical Guide, 5th Edition', Wiley-Blackwell, 2015.  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.  S. Arlot and A. Celisse, 'A survey of cross-validation procedures for model selection', Statistics Surveys, vol. 4, pp. 40-79, 2010.  T. Ahonen, A. Hadid, and M. Pietikäinen, 'Face Recognition with Local Binary Patterns', European Conference on Computer Vision, pp. 469-481, 2004.  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.  R. C. Gonzalez and R. E. Woods, 'Digital Image Processing', Prentice Hall, 2002.  R. C. Gonzalez, R. E. Woods, and S. L. Eddins, 'Digital Image Processing Using MATLAB', Pearson Prentice Hall, 2013.||摘要:||
Blood 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%.
|Appears in Collections:||通訊工程研究所|
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