Automatic Detection and Characterization of Parasite Eggs using Deep Learning Methods
Apichon Kitvimonrat, Natthaporn Hongcharoen, Sanparith Marukatat, Sarin Watcharabutsarakham · 2020
Parasitic infection can be detected by analyzing the microscopic image of fecal slide. The number of parasite eggs found in the slide will be used to determine the degree of infection. Opisthorchis Vivertini (OV) and Minute Intestinal Flukes (MIF) are among the most common parasites found in southeast Asia. OV affects liver whereas MIF affect small intestine. The egg of both parasites have almost the same size and shape which make it difficult to discriminate between them, even for experts. The detection and classification of parasite eggs in the microscopic image of the fecal slides can be seen as object detection problem. This work investigates the application of state-of-the-art object detectors to this task. The experimental results show that these deep learning models are capable of correctly detecting and classifying the parasite eggs.