Abstract：As the most common gynecological examination items, leucorrhea routine examination has a wide application and an important position in clinical testing. In view of the importance of leukocytes in clinical medicine and the many deficiencies of current detection methods, this paper focuses on the automatic detection of leukocytes. Microscopic images were obtained in a local hospital through a leucorrhea automatic detector. After filtering, the images were enhanced and segmented. The sample library was established, and the feature extraction and classification were done based on the convolution neural network. Finally, the validity of the method was verified by cross validation. In the automatic detection of leukocytes, for a dataset consisting of twenty thousand samples, our proposed method achieved 95% in sensitivity, 84% in specificity and 89.5% in accuracy, which meet the requirement of medical clinical testing. The digital image processing technology and the convolution neural network are applied to the detection of leukocytes in medical microscopic images. The proposed method solves the key problem of characteristic expression, verifies the feasibility of automatic identification, and improves the quality and efficiency of detection.
钟亚, 张静, 肖峻. 基于卷积神经网络的白带中白细胞的自动检测[J]. 中国生物医学工程学报, 2018, 37(2): 163-168.
Zhong Ya, Zhang Jing, Xiao Jun. Automatic Detection of Leukocytes in Leucorrhea Based on Convolution Neural Network. Chinese Journal of Biomedical Engineering, 2018, 37(2): 163-168.
[1] 万国忠. 857例白带常规检查结果分析 [J]. 中国药物经济学,2013(2): 470-471. [2] 邓霭静. 中医诊治带下病学术源流探讨及文献整理研究 [D].广州:广州中医药大学, 2014. [3] 李瑞珍. 白带常规检查的意义与局限性 [J]. 四川医学, 2014, 35(1): 106-107. [4] 丁高侬. 三种妇科白带检查方法比较分析 [J]. 中国现代医药杂志,2011,13: 52-53. [5] 阮君,陆静芬. 临床阴道分泌物检测方法的比较 [J]. 中国伤残医学,2014(1): 14-16. [6] 凌晓午,罗静,苏莉,等. 2种方法检测白带常规多项目结果对比分析 [J]. 中国卫生检验杂志,2015(13): 2136-2138. [7] Irshad H, Veillard A, Roux L, et al. Methods for nuclei detection, segmentation, and classification in digital histopathology: a review-current status and future potential [J]. IEEE Reviews in Biomedical Engineering, 2014, 7: 97-114. [8] Gautam A, Singh P, Raman B, et al. Automatic classification of leukocytes using morphological features and nave Bayes classifier [C] //Region 10 Conference(TENCON). Singapore City: IEEE, 2016: 1023-1027. [9] Tsalis R, Agus A, Nopriadi, et al. Classification of leukocyte images using K-means clustering based on geometry features [C] // Engineer Seminar(InAES). Yogyakarta: IEEE, 2016: 245-249. [10] Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition [J]. Proceeding of the IEEE, 1998, 86(11): 2278-2324. [11] Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks [C] //Advances in Neural Information Processing Systems. Lake Tahoe: Neural Information Processing SystemFoundation, 2012: 1097-1105. [12] Bouvrie J. Notes on Convolutional Neural Networks[EB/OL]. http://cogprints.org/5869/2011-03-11/2017-07-06. [13] Kirsch RA. Computer determination of the constituent structure of biological images [J]. Computers & Biomedical Research, 1971, 4(4): 315-328. [14] Otsu N. Threshold selection method from gray-level histograms [J]. IEEE Transaction on Systems Man and Cybernetics, 1979, 9(1): 62-66. [15] Keys R. Cubic convolution interpolation for digital image processing [J]. IEEE Transactions on Acoustics Speech & Signal Processing, 2003, 29(6): 1153-1160. [16] Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks[J]. Journal of Machine Learning Research, 2010, 9: 249-256. [17] Ar J. Statistical comparisons of classifiers over multiple data sets[J]. Journal of Machine Learning Research, 2006, 7(1): 1-30. [18] Fawcett T. An introduction to ROC analysis [J]. Pattern Recognition Letters, 2006, 27(8): 861-874. [19] Geisler WM, Yu S, Venglarik M, et al. Vaginal leucocyte counts in women with bacterial vaginosis: relation to vaginal and cervical infections [J]. Sex Transmit Infect, 2004, 80: 401-405. [20] Ananthi VP, Balasubramaniam P. A new thresholding technique based on fuzzy set as an application to leukocyte nucleus segmentation [J]. Computer Methods & Programs in Biomedicine, 2016, 134: 165-177. [21] Vogado LHS, Veras RDMS, Andrade AR, et al. Unsupervised leukemia cells segmentation based on multi-space color channels [C] //IEEE International Symposium on Multimedia. San Jose: IEEE, 2016: 451-456.
