Real-Time Protozoa Detection from Microscopic Imaging Using YOLOv4 Algorithm
Yazarlar (3)
Dr. Öğr. Üyesi İdris KAHRAMAN Karabük Üniversitesi, Türkiye
Prof. Dr. İsmail Rakıp KARAŞ Karabük Üniversitesi, Türkiye
Doç. Dr. Muhammed Kamil TURAN Karabük Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Applied Sciences Switzerland (Q2) | ||
| Dergi ISSN | 2076-3417 Dergi Bilgileri (2024) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 01-2024 |
| Cilt / Sayı / Sayfa | 14 / 2 / 607–0 | DOI | 10.3390/app14020607 |
| Makale Linki | https://doi.org/10.3390/app14020607 | ||
| UAK Araştırma Alanları |
Görüntü İşleme
Yapay Zeka
Biyoenformatik
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| Özet |
| Protozoa 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 … |
| Anahtar Kelimeler |
| convolutional neural network | deep learning | medical image processing | protozoa detection | protozoan parasite dataset | yolo |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 7 |
| Scopus | 8 |
| Google Scholar | 12 |