A deep contrastive learning-based image retrieval system for automatic detection of infectious cattle diseases

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dc.contributor.authorVeerayuth Kittichai
dc.contributor.authorMorakot Kaewthamasorn
dc.contributor.authorApinya Arnuphaprasert
dc.contributor.authorRangsan Jomtarak
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.accessioned2025-03-10T07:22:48Z
dc.date.available2025-03-10T07:22:48Z
dc.date.issued2025
dc.description.abstractAnaplasmosis, which is caused by Anaplasma spp. and transmitted by tick bites, is one of the most serious livestock animal diseases worldwide, causing significant economic losses as well as public health issues. Anaplasma marginale, a gram-negative intracellular obligate bacterium, can cause disease in cattle and other ruminants. Because of the insufficient quality of the slides, a microscopic diagnostic procedure is time-consuming and challenging to diagnose. Intra- and inter-rater variation is frequently imposed on by technicians who are underqualified and unexperienced. Alternatively, algorithms could support local employees in tracking disease transmission and quick action, especially in Thailand where this cattle disease is common. As a result, the study intends to create an automated tool based on a deep neural network linked with an image-retrieval procedure for recognizing infections in microscopic pictures. The Resnext-50 model, which serves as the embedding spaceÕs backbone and is optimized by Triplet-Margin loss, outperforms, with averaged accuracy and specificity ratings of 91.30 percent and 92.83 percent, respectively. The modelÕs performance was also improved by a fine-tuned procedure between k-nearest neighbor and its normalized distance of each data point, including precision of 0.833 ± 0.134, specificity of 0.930 ± 0.054, recall of 0.838 ± 0.118, and accuracy of 0.915 ± 0.025, respectively. Five-fold cross-validation confirms that the trained model using the optimal k-nearest neighbor (kNN) for the image-based retrieval system, involving 12 images, prevents overfitting via dataset variations indicating areas under the receiver operating curve rankings ranging from 0.917 to 0.922. The image retrieval technique demonstrated in this research is a prototype for a variety of applications. The findings may aid in the early diagnosis of anaplasmosis infections in remote areas without access to veterinary care or costly molecular diagnostic tools. © The Author(s) 2024.
dc.identifier.citationJournal of Big Data
dc.identifier.doi10.1186/s40537-024-01057-7
dc.identifier.issn21961115
dc.identifier.scopus2-s2.0-85214096267
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/4442
dc.languageEnglish
dc.publisherSpringer Nature
dc.rightsAll Open Access; Gold Open Access
dc.rights.holderScopus
dc.subjectAn image retrieval procedure
dc.subjectAnaplasmosis
dc.subjectAutomatic tools
dc.subjectDeep contrastive learning
dc.subjectDeep neural network
dc.subjectTriplet margin loss
dc.titleA deep contrastive learning-based image retrieval system for automatic detection of infectious cattle diseases
dc.typeArticle
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85214096267&doi=10.1186%2fs40537-024-01057-7&partnerID=40&md5=c8c67b6ba894ed1f13f9ca0be133728c
oaire.citation.issue1
oaire.citation.volume12
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