Abstract

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Title:: Classification and counting of Gram-stained bacteria by deeply learning in micro-image

Author(s):: DONG Yubo1; WANG Rui1; ZHAO Huijuan2; ZHANG Shujing3; 1. School of Opto-Electronic Information Science and Technology, Yantai University, Yantai 264005, China 2. School of Public Health and Management, Binzhou Medical College, Yantai 264005, China 3. College of Career Technology of Hebei Normal University, Shijiazhuang 050000, China

Keywords:: Keywords: Gram-stained bacteria classification & counting U-Net ResNet deep learning

Abstract:: Abstract: 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 Gram-negative 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.