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

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dc.contributor.authorKittichai, Veerayuth
dc.contributor.authorKaewthamasorn, Morakot
dc.contributor.authorArnuphaprasert, Apinya
dc.contributor.authorJomtarak, Rangsan
dc.contributor.authorNaing, Kaung Myat
dc.contributor.authorTongloy, Teerawat
dc.contributor.authorChuwongin, Santhad
dc.contributor.authorBoonsang, Siridech
dc.date.accessioned2026-08-06T10:52:46Z
dc.date.available2026-08-06T10:52:46Z
dc.date.issued2025-12-01
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 space’s 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 model’s 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.
dc.identifier.citationJournal of Big Data, 12(1), 2025
dc.identifier.doi10.1186/s40537-024-01057-7
dc.identifier.issn21961115
dc.identifier.other2-s2.0-85214096267
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/17386
dc.sourceJournal of Big Data
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

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