Automatic identification of medically important mosquitoes using embedded learning approach-based image-retrieval system

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dc.contributor.authorVeerayuth Kittichai
dc.contributor.authorMorakot Kaewthamasorn
dc.contributor.authorYudthana Samung
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.accessioned2025-03-10T07:34:45Z
dc.date.available2025-03-10T07:34:45Z
dc.date.issued2023
dc.description.abstractMosquito-borne diseases such as dengue fever and malaria are the top 10 leading causes of death in low-income countries. Control measure for the mosquito population plays an essential role in the fight against the disease. Currently, several intervention strategies; chemical-, biological-, mechanical- and environmental methods remain under development and need further improvement in their effectiveness. Although, a conventional entomological surveillance, required a microscope and taxonomic key for identification by professionals, is a key strategy to evaluate the population growth of these mosquitoes, these techniques are tedious, time-consuming, labor-intensive, and reliant on skillful and well-trained personnel. Here, we proposed an automatic screening, namely the deep metric learning approach and its inference under the image-retrieval process with Euclidean distance-based similarity. We aimed to develop the optimized model to find suitable miners and suggested the robustness of the proposed model by evaluating it with unseen data under a 20-returned image system. During the model development, well-trained ResNet34 are outstanding and no performance difference when comparing five data miners that showed up to 98% in its precision even after testing the model with both image sources: stereomicroscope and mobile phone cameras. The robustness of the proposedÑtrained model was tested with secondary unseen data which showed different environmental factors such as lighting, image scales, background colors and zoom levels. Nevertheless, our proposed neural network still has great performance with greater than 95% for sensitivity and precision, respectively. Also, the area under the ROC curve given the learning system seems to be practical and empirical with its value greater than 0.960. The results of the study may be used by public health authorities to locate mosquito vectors nearby. If used in the field, our research tool in particular is believed to accurately represent a real-world scenario. © 2023, The Author(s).
dc.identifier.citationScientific Reports
dc.identifier.doi10.1038/s41598-023-37574-3
dc.identifier.issn20452322
dc.identifier.scopus2-s2.0-85163762489
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/4590
dc.languageEnglish
dc.publisherNature Research
dc.rightsAll Open Access; Gold Open Access; Green Open Access
dc.rights.holderScopus
dc.titleAutomatic identification of medically important mosquitoes using embedded learning approach-based image-retrieval system
dc.typeArticle
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85163762489&doi=10.1038%2fs41598-023-37574-3&partnerID=40&md5=55edb6462cb3c54ff0d2abf98111d641
oaire.citation.issue1
oaire.citation.volume13
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