Classification for avian malaria parasite Plasmodium gallinaceum blood stages by using deep convolutional neural networks

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dc.contributor.authorVeerayuth Kittichai
dc.contributor.authorMorakot Kaewthamasorn
dc.contributor.authorSuchansa Thanee
dc.contributor.authorRangsan Jomtarak
dc.contributor.authorKamonpob Klanboot
dc.contributor.authorKaung Myat Naing
dc.contributor.authorTeerawat Tongloy
dc.contributor.authorSanthad Chuwongin
dc.contributor.authorSiridech Boonsang
dc.contributor.correspondenceS. Boonsang; Department of Electrical Engineering, School of Engineering, King MongkutÕs Institute of Technology Ladkrabang, Bangkok, Thailand; email: Siridech.bo@kmitl.ac.th
dc.date.accessioned2025-03-10T07:35:28Z
dc.date.available2025-03-10T07:35:28Z
dc.date.issued2021
dc.description.abstractThe infection of an avian malaria parasite (Plasmodium gallinaceum) in domestic chickens presents a major threat to the poultry industry because it causes economic loss in both the quality and quantity of meat and egg production. Computer-aided diagnosis has been developed to automatically identify avian malaria infections and classify the blood infection stage development. In this study, four types of deep convolutional neural networks, namely Darknet, Darknet19, Darknet19-448 and Densenet201 are used to classify P. gallinaceum blood stages. We randomly collected a dataset of 12,761 single-cell images consisting of three parasite stages from ten-infected blood films stained by Giemsa. All images were confirmed by three well-trained examiners. The study mainly compared several image classification models and used both qualitative and quantitative data for the evaluation of the proposed models. In the model-wise comparison, the four neural network models gave us high values with a mean average accuracy of at least 97%. The Darknet can reproduce a superior performance in the classification of the P. gallinaceum development stages across any other model architectures. Furthermore, the Darknet has the best performance in multiple class-wise classification, with average values of greater than 99% in accuracy, specificity, and sensitivity. It also has a low misclassification rate (< 1%) than the other three models. Therefore, the model is more suitable in the classification of P. gallinaceum blood stages. The findings could help us create a fast-screening method to help non-experts in field studies where there is a lack of specialized instruments for avian malaria diagnostics. © 2021, The Author(s).
dc.identifier.citationScientific Reports
dc.identifier.doi10.1038/s41598-021-96475-5
dc.identifier.issn20452322
dc.identifier.scopus2-s2.0-85113246079
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/4703
dc.languageEnglish
dc.publisherNature Research
dc.rightsAll Open Access; Gold Open Access; Green Open Access
dc.rights.holderScopus
dc.titleClassification for avian malaria parasite Plasmodium gallinaceum blood stages by using deep convolutional neural networks
dc.typeArticle
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85113246079&doi=10.1038%2fs41598-021-96475-5&partnerID=40&md5=826e0b86aba8b64a4e8216eb34e2fbc5
oaire.citation.issue1
oaire.citation.volume11
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