SCOPUS 2025

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Browsing SCOPUS 2025 by Subject "Deep neural network"

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    A deep contrastive learning-based image retrieval system for automatic detection of infectious cattle diseases
    (Springer Nature, 2025) Veerayuth Kittichai; Morakot Kaewthamasorn; Apinya Arnuphaprasert; Rangsan Jomtarak; Kaung Myat Naing; Teerawat Tongloy; Santhad Chuwongin; Siridech Boonsang; S. Boonsang; Department of Electrical Engineering, School of Engineering, King MongkutÕs Institute of Technology Ladkrabang, Bangkok, Thailand; email: siridech.bo@kmitl.ac.th
    Anaplasmosis, 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.