Automatic classification of cells in microscopic fecal images using convolutional neural networks

Xiaohui Du, Lin Liu, Xiangzhou Wang, Guangming Ni, Jing Zhang, Ruqian Hao, Juanxiu Lin, Yong Liu
2019 Bioscience Reports  
The analysis of fecal-type components for clinical diagnosis is important. The main examination involves the counting of red blood cells (RBCs), white blood cells (WBCs), and molds under the microscopic. With the development of machine vision, some vision-based detection schemes have been proposed. However, these methods have a single target for detection, with low detection efficiency and low accuracy. We proposed an algorithm to identify the visible image of fecal composition based on
more » ... ent deep learning. The algorithm mainly includes region proposal and candidate recognition. In the process of segmentation, we proposed a morphology extraction algorithm in a complex background. As for the candidate recognition, we proposed a new convolutional neural network (CNN) architecture based on Inception-v3 and principal component analysis (PCA). This method achieves high-average Precision of 90.7%, which is better than the other mainstream CNN models. Finally, the images within the rectangle marks were obtained. The total time for detection of an image was roughly 1200 ms. The algorithm proposed in the present paper can be integrated into an automatic fecal detection system.
doi:10.1042/bsr20182100 pmid:30872411 pmcid:PMC6449518 fatcat:7ssi4hrj4nai5ereejq6hwrcxa