Mobile Bot Application for Identification of Trypanosoma evansi Infection through Thin-Blood Film Examination Based on Deep Learning Approach

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dc.contributor.authorJomtarak, Rangsan
dc.contributor.authorKittichai, Veerayuth
dc.contributor.authorKaewthamasorn, Morakot
dc.contributor.authorThanee, Suchansa
dc.contributor.authorArnuphapprasert, Apinya
dc.contributor.authorNaing, Kaung Myat
dc.contributor.authorTongloy, Teerawat
dc.contributor.authorBoonsang, Siridech
dc.contributor.authorChuwongin, Santhad
dc.date.accessioned2026-08-06T10:40:06Z
dc.date.available2026-08-06T10:40:06Z
dc.date.issued2023-01-01
dc.description.abstractTrypanosomiasis caused Trypanosoma evansi is current public health concern especially, in south Asia and Southeast Asia. Although polymerase chain reaction is currently used as a standard method, the techniques required skilled personnel, were performed in multiple steps, and required expensive instruments. Fundamental microscopic approach also has limitation in use by facing both inter- and intra-variability of interpretation by examiners. New automatic tool with the microscopic examination is needed. The study aimed to develop the mobile application-based YOLO neural network algorithms to predict T. evansi blood stages from thin-blood film examination. YOLO v4 tiny model is outperformed to localize and classify unseen images with the best performance at 95% of sensitivity, specificity, precision, accuracy and F1 score, respectively, with less misclassification rate than 5%. Simulation implementation platform, calling CiRA bot, give the empirical result and reliably comparable to that from the computational experiment studied with the area under ROC and precision-recall curves as 0.964 and 0.962, respectively. The result obtained from the CIRA bot platform is good enough for further distribution in field site. In the future, the study could contribute human and animal public health staff to simply identify the unicellular parasitic flagellate infection and also benefit them for designing the strategy in prevention and treatment of the disease.
dc.identifier.citationInternational Conference on Cybernetics and Innovations Icci 2023, 2023
dc.identifier.doi10.1109/ICCI57424.2023.10112327
dc.identifier.other2-s2.0-85159860271
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/14046
dc.sourceInternational Conference on Cybernetics and Innovations Icci 2023
dc.subjecta microscopic examination
dc.subjectMobile application
dc.subjectmodel performance
dc.subjectTrypanosoma evansi
dc.subjectYOLO algorithms
dc.titleMobile Bot Application for Identification of Trypanosoma evansi Infection through Thin-Blood Film Examination Based on Deep Learning Approach
dc.typeConference Paper

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