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.
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