Real-Time Protozoa Detection from Microscopic Imaging Using YOLOv4 Algorithm

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dc.authoridKahraman, Idris/0000-0003-1121-0153
dc.contributor.authorKahraman, Idris
dc.contributor.authorKaras, Ismail Rakip
dc.contributor.authorTuran, Muhammed Kamil
dc.date.accessioned2024-09-29T16:08:04Z
dc.date.available2024-09-29T16:08:04Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractProtozoa detection and classification from freshwaters and microscopic imaging are critical components in environmental monitoring, parasitology, science, biological processes, and scientific research. Bacterial and parasitic contamination of water plays an important role in society health. Conventional methods often rely on manual identification, resulting in time-consuming analyses and limited scalability. In this study, we propose a real-time protozoa detection framework using the YOLOv4 algorithm, a state-of-the-art deep learning model known for its exceptional speed and accuracy. Our dataset consists of objects of the protozoa species, such as Bdelloid Rotifera, Stylonychia Pustulata, Paramecium, Hypotrich Ciliate, Colpoda, Lepocinclis Acus, and Clathrulina Elegans, which are in freshwaters and have different shapes, sizes, and movements. One of the major properties of our work is to create a dataset by forming different cultures from various water sources like rainwater and puddles. Our network architecture is carefully tailored to optimize the detection of protozoa, ensuring precise localization and classification of individual organisms. To validate our approach, extensive experiments are conducted using real-world microscopic image datasets. The results demonstrate that the YOLOv4-based model achieves outstanding detection accuracy and significantly outperforms traditional methods in terms of speed and precision. The real-time capabilities of our framework enable rapid analysis of large-scale datasets, making it highly suitable for dynamic environments and time-sensitive applications. Furthermore, we introduce a user-friendly interface that allows researchers and environmental professionals to effortlessly deploy our YOLOv4-based protozoa detection tool. We conducted f1-score 0.95, precision 0.92, sensitivity 0.98, and mAP 0.9752 as evaluating metrics. The proposed model achieved 97% accuracy. After reaching high efficiency, a desktop application was developed to provide testing of the model. The proposed framework's speed and accuracy have significant implications for various fields, ranging from a support tool for paramesiology/parasitology studies to water quality assessments, offering a powerful tool to enhance our understanding and preservation of ecosystems.en_US
dc.description.sponsorshipScientific Research Projects Unit of the Karabk University [KB-BAP-16/2-DR-102]en_US
dc.description.sponsorshipThis work was supported by Scientific Research Projects Unit of the Karabuek University, Project Number: KBUE-BAP-16/2-DR-102.en_US
dc.identifier.doi10.3390/app14020607
dc.identifier.issn2076-3417
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85192472826en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/app14020607
dc.identifier.urihttps://hdl.handle.net/20.500.14619/7325
dc.identifier.volume14en_US
dc.identifier.wosWOS:001149157100001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofApplied Sciences-Baselen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectdeep learningen_US
dc.subjectprotozoa detectionen_US
dc.subjectmedical image processingen_US
dc.subjectprotozoan parasite dataseten_US
dc.subjectyoloen_US
dc.subjectconvolutional neural networken_US
dc.titleReal-Time Protozoa Detection from Microscopic Imaging Using YOLOv4 Algorithmen_US
dc.typeArticleen_US

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