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Leucorrhea-wet-film recognition based on coarse-to-fine CNN-SVM

机译:基于粗至细胞CNN-SVM的白带 - 湿膜识别

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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:念珠菌和白细胞是使用白带湿膜诊断微观图像中妇科炎症的两个重要指标。然而,在白带湿薄膜的微观形象中,目标和背景之间的对比度微不足道,纹理,边缘弱,整体下降灰色等,使白细胞和念珠菌的智能检测在白带的微观形象中的智能检测湿膜极困难。为了解决问题,我们提出了一种基于粗致细的CNN-SVM的检测方法,其中薄膜用形态开口操作员预过滤,然后通过使用Hough圆检测来鉴定白细胞,最后,基于粗至细的CNN-SVM来实现Candida的特征提取和分类。专业人员也提供了展示所提出的方法的性能。

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