Zhang Jing, Lu Songhan, Wang Xiangzhou, Du Xiaohui, Ni Guangming, Liu Juanxiu, Liu Lin, Liu Yong
J Opt Soc Am A Opt Image Sci Vis. 2017 Sep 1;34(9):1484-1489. doi: 10.1364/JOSAA.34.001484.
Identifying fungi in microscopic leucorrhea images provides important information for evaluating gynecological diseases. Subjective judgment and fatigue can greatly affect recognition accuracy. This paper proposes an automatic identification system to detect fungi in leucorrhea images that incorporates a convolutional neural network, the histogram of oriented gradients algorithm, and a binary support vector machine. In experiments, the detection rate of the positive samples was as high as 99.8%. The experimental results demonstrate the effectiveness of the proposed method and its potential as a primary software component of a completely automated system.
在微观白带图像中识别真菌可为评估妇科疾病提供重要信息。主观判断和疲劳会极大地影响识别准确性。本文提出了一种用于检测白带图像中真菌的自动识别系统，该系统结合了卷积神经网络、方向梯度直方图算法和二元支持向量机。在实验中，阳性样本的检测率高达99.8%。实验结果证明了所提方法的有效性及其作为完全自动化系统主要软件组件的潜力。
