摘要
菌种和数量是研究菌群失调和疾病预测的重要参数,然而细菌分类和计数工作主要由人工完成,过程繁琐,极易出错,并且耗时费力。本研究提出一种基于图像深度学习的方法对显微图像中的革兰氏阳性杆菌、革兰氏阴性杆菌、革兰氏阳性球菌和革兰氏阴性球菌进行分类。整个算法过程包括分割和分类识别两部分,首先采用U-Net"渐进分割法"对细菌部分和背景部分进行分割;然后将分割后的细菌分别投入ResNet50模型和VGG19模型进行识别和计数。将经过再训练ResNet50模型和VGG19模型的计数结果与人工分类计数标准的结果进行比较,实验结果表明ResNet50模型可以达到人工分类和计数的准确率。
Breeds and quantity of bacteria are important parameters for research of dysbacteriosis as well as disease prediction.However,the classification and counting of bacteria was a cumbersome task mainly done by humans,and the process is error-prone,time-consuming and laborious.In this paper,a method based on image deep learning was proposed to classify the four types of bacteria including Gram-positive bacilli,Gram-negative bacilli,Gram-positive cocci and Gramnegative cocci in the microscopic images.The method consists of two major procedures:one is segmentation and the other is classification and identification.First,U-Net"progressive segmentation"was used to segment the bacteria part and the background part.Second,the segmented bacteria were fed into ResNet50 model and VGG19 model for recognition and counting.Finally,the results from retrained ResNet50 model and retrained VGG19 model were compared with the manual classification counting standard,and the results from retrained ResNet50 model were shown to reach the accuracy of manual counting and classification.
作者
董宇波
王蕊
赵慧娟
张书景
DONG Yubo;WANG Rui;ZHAO Huijuan;ZHANG Shujing(School of Opto-Electronic Information Science and Technology,Yantai University,Yantai 264005,China;School of Public Health and Management,Binzhou Medical College,Yantai 264005,China;College of Career Technology of Hebei Normal University,Shijiazhuang 050000,China)
出处
《中国医学物理学杂志》
CSCD
2021年第1期127-132,共6页
Chinese Journal of Medical Physics
基金
国家自然科学基金(61701165,61771181)
山东省自然科学基金(ZR2017BF040)。
