Automatic recognition of parasitic products in stool examination using object detection approach
Issued Date
2022-01-01
Resource Type
eISSN
23765992
Scopus ID
2-s2.0-85137630375
Journal Title
PeerJ Computer Science
Volume
8
Rights Holder(s)
SCOPUS
Bibliographic Citation
PeerJ Computer Science Vol.8 (2022)
Suggested Citation
Naing K.M., Boonsang S., Chuwongin S., Kittichai V., Tongloy T., Prommongkol S., Dekumyoy P., Watthanakulpanich D. Automatic recognition of parasitic products in stool examination using object detection approach. PeerJ Computer Science Vol.8 (2022). doi:10.7717/PEERJ-CS.1065 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/87511
Title
Automatic recognition of parasitic products in stool examination using object detection approach
Other Contributor(s)
Abstract
Background. Object detection is a new artificial intelligence approach to morpho- logical recognition and labeling parasitic pathogens. Due to the lack of equipment and trained personnel, artificial intelligence innovation for searching various parasitic products in stool examination will enable patients in remote areas of undeveloped countries to access diagnostic services. Because object detection is a developing approach that has been tested for its effectiveness in detecting intestinal parasitic objects such as protozoan cysts and helminthic eggs, it is suitable for use in rural areas where many factors supporting laboratory testing are still lacking. Based on the literatures, the YOLOv4-Tiny produces faster results and uses less memory with the support of low- end GPU devices. In comparison to the YOLOv3 and YOLOv3-Tiny models, this study aimed to propose an automated object detection approach, specifically the YOLOv4- Tiny model, for automatic recognition of intestinal parasitic products in stools. Methods. To identify protozoan cysts and helminthic eggs in human feces, the three YOLO approaches; YOLOv4-Tiny, YOLOv3, and YOLOv3-Tiny, were trained to recognize 34 intestinal parasitic classes using training of image dataset. Feces were processed using a modified direct smear method adapted from the simple direct smear and the modified Kato-Katz methods. The image dataset was collected from intestinal parasitic objects discovered during stool examination and the three YOLO models were trained to recognize the image datasets. Results. The non-maximum suppression technique and the threshold level were used to analyze the test dataset, yielding results of 96.25% precision and 95.08% sensitivity for YOLOv4-Tiny. Additionally, the YOLOv4-Tiny model had the best AUPRC performance of the three YOLO models, with a score of 0.963. Conclusion. This study, to our knowledge, was the first to detect protozoan cysts and helminthic eggs in the 34 classes of intestinal parasitic objects in human stools.