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首页> 外文会议>2017 International Conference on Security, Pattern Analysis, and Cybernetics >Leucorrhea-wet-film recognition based on coarse-to-fine CNN-SVM
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Leucorrhea-wet-film recognition based on coarse-to-fine CNN-SVM

机译：基于粗细CNN-SVM的白带湿膜识别

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摘要

Candida and leukocyte are two important indicators in the diagnosis of gynecological inflammation in microscopic images using the leucorrhea wet film. However, in the microscopic image of leucorrhea wet films, insignificant contrast between target and background, slight differences in texture, weak edges, drab gray on the whole, etc., make intelligent detection of white blood cells and Candida in the microscopic image of leucorrhea wet film extremely difficult. To tackle the problem, we propose a detection method based on coarse-to-fine CNN-SVM, in which the films are pre-filtered with a morphological opening operator, and then white blood cells are identified by using Hough circle detection, and finally, the feature extraction and classification of Candida are implemented based on coarse-to-fine CNN-SVM. Experminents results are also provide to demonstrate the performance of the proposed method.
机译：在使用白带湿膜的显微图像中，念珠菌和白细胞是诊断妇科炎症的两个重要指标。然而，在白带湿膜的显微图像中，目标与背景之间的对比度不明显，质地略有不同，边缘薄弱，整体呈浅褐色等，使得白带显微图像中白细胞和念珠菌的智能检测成为可能。湿膜极其困难。为了解决这个问题，我们提出了一种基于粗糙到精细的CNN-SVM的检测方法，其中将膜用形态学打开算子进行预过滤，然后使用霍夫圆检测法识别白细胞，最后，基于粗糙到精细的CNN-SVM实现了念珠菌的特征提取和分类。还提供了实验结果来证明所提出方法的性能。

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