Öz
Intestinal parasites represent a significant public health issue worldwide, with particularly high prevalence in tropical and developing countries. These parasites reside in the host's intestinal system, feeding and causing various health problems; if left untreated, they can lead to serious complications such as vomiting, diarrhea, weakness, and developmental disorders in children. Therefore, timely detection of parasitic infections is critical for protecting individual health and improving public health outcomes. Traditional microscopic examination methods face challenges such as low efficiency and reliance on expert interpretation. In this context, artificial intelligence-based automated detection systems enhance the efficiency of healthcare services by enabling the rapid and accurate identification of parasites. This study examines the integration of deep learning and machine learning methods using the Chula-Parasite-Egg-11 dataset, and the results demonstrate significant performance improvements in parasite detection through these approaches. The highest performance was achieved with the ViT-FPN-supported YOLO11m model, with an F1 score of 99.53% and an mAP score of 95.62%. Additionally, combining the outputs of deep learning-based approaches with machine learning classifiers improved accuracy rates, making resource utilization more efficient. This research highlights the effectiveness of artificial intelligence applications in parasite detection, making a significant contribution to the literature.
Anahtar Kelimeler
intestinal parasite artificial intelligence deep learning classification YOLO
Proje Numarası
1919B012466699
Kaynakça
- AlDahoul, N., Abdul Karim, H., Kee, S. L., & Tan, M. J. T. (2022). Localization and classification of parasitic eggs in microscopic images using an EfficientDet detector. 2022 IEEE International Conference on Image Processing (ICIP), 1922–1926. https://doi.org/10.1109/ICIP46576.2022.9897844
- AlDahoul, N., Abdul Karim, H., Momo, M. A., Escobar, F. I. F., Magallanes, V. A., & Tan, M. J. T. (2023). Parasitic egg recognition using convolution and attention network. Scientific Reports, 13, 14475. https://doi.org/10.1038/s41598-023-41711-3
- Anantrasirichai, N., Chalidabhongse, T. H., Palasuwan, D., Naruenatthanaset, K., Kobchaisawat, T., Nunthanasup, N., Boonpeng, K., Ma, X., & Achim, A. (2022). ICIP 2022 challenge on parasitic egg detection and classification in microscopic images: Dataset, methods and results. IEEE International Conference on Image Processing (ICIP). https://doi.org/10.21227/vyh8-4h71
- Belete, Y. A., Kassa, T. Y., & Baye, M. F. (2021). Prevalence of intestinal parasite infections and associated risk factors among patients of Jimma health center requested for stool examination, Jimma, Ethiopia. PLoS ONE, 16, e247063.
- Lane, M., Kashani, M., Barratt, J. L., Qvarnstrom, Y., Yabsley, M. J., Garrett, K. B., et al. (2023). Application of a universal parasite diagnostic test to biological specimens collected from animals. International Journal for Parasitology: Parasites and Wildlife, 20, 20–30.
- Pedraza, A., Ruiz-Santaquiteria, J., Deniz, O., & Bueno, G. (2022). Parasitic egg detection and classification with transformer-based architectures. 2022 IEEE International Conference on Image Processing (ICIP), 1942–1946. https://doi.org/10.1109/ICIP46576.2022.9897846
- Pinetsuksai, N., Jaksukam, K., Kittichai, V., Tongloy, T., Chuwongin, S., Jomtarak, R., & Boonsang, S. (2023). Development of self-supervised learning with Dinov2-distilled models for parasite classification in screening. 2023 15th International Conference on Information Technology and Electrical Engineering (ICITEE). https://doi.org/10.1109/ICITEE59582.2023.10317719
- Pratama, Y., Fujimura, Y., Funatomi, T., & Mukaigawa, Y. (2022). Parasitic egg detection and classification by utilizing the YOLO algorithm with deep latent space image restoration and GrabCut augmentation. 2022 IEEE International Conference on Image Processing (ICIP), 1881–1885. https://doi.org/10.1109/ICIP46576.2022.9897645
- Rajasekar, S. J. S., Jaswal, G., Perumal, V., Ravi, S., & Dutt, V. (2023). Parasite.ai – An automated parasitic egg detection model from microscopic images of fecal smears using deep learning techniques. 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI). https://doi.org/10.1109/ACCAI58221.2023.10200869
- Ruiz-Santaquiteria, J., Pedraza, A., Vallez, N., & Velasco, A. (2022). Parasitic egg detection with a deep learning ensemble. 2022 IEEE International Conference on Image Processing (ICIP), 1937–1941. https://doi.org/10.1109/ICIP46576.2022.9897858
- Suzuki, C. T., Gomes, J. F., Falcao, A. X., Papa, J. P., & Hoshino-Shimizu, S. (2012). Automatic segmentation and classification of human intestinal parasites from microscopy images. IEEE Transactions on Biomedical Engineering, 60(3), 803–812.
- Taghipour, A., Javanmard, E., Rahimi, H. M., Abdoli, A., Matin, S., Haghbin, M., et al. (2024). Prevalence of intestinal parasitic infections in patients with diabetes: A systematic review and meta-analysis. International Health, 16, 23–34.
- Tureckova, A., Turecek, T., & Oplatkova, Z. K. (2022). ICIP 2022 challenge: PEDCMI, TOOD enhanced by slicing-aided fine-tuning and inference. IEEE International Conference on Image Processing (ICIP) Proceedings. https://doi.org/10.1109/ICIP46576.2022.9897826
- World Health Organization. (2023). Soil-transmitted helminth infections. https://www.who.int/news-room/fact-sheets/detail/soil-transmitted-helminth-infections
- Xu, W., Zhai, Q., Liu, J., Xu, X., & Hua, J. (2024). A lightweight deep-learning model for parasite egg detection in microscopy images. Parasites & Vectors, 17, 454. https://doi.org/10.1186/s13071-024-06503-2
Öz
Bağırsak parazitleri, dünya genelinde önemli bir halk sağlığı sorunu oluşturarak, özellikle tropik ve gelişmekte olan ülkelerde yüksek yayılım göstermektedir. Bu parazitler, konakçıların bağırsak sisteminde yaşar ve beslenerek çeşitli sağlık sorunlarına yol açar; tedavi edilmediğinde kusma, ishal, güçsüzlük ve çocuk gelişiminde bozukluk gibi ciddi komplikasyonlar doğurabilir. Bu nedenle, parazit enfeksiyonlarının zamanında tespiti, bireylerin sağlık durumunu korumak ve toplum sağlığını iyileştirmek için kritik öneme sahiptir. Geleneksel mikroskobik inceleme yöntemleri, düşük verimlilik ve uzman bağımlılığı gibi sorunlarla karşı karşıyadır. Bu bağlamda, yapay zeka tabanlı otomatik tespit sistemleri, parazitlerin hızlı ve doğru bir şekilde tanımlanmasını sağlayarak sağlık hizmetlerinin etkinliğini artırmaktadır. Bu çalışma, Chula-Parasite-Egg-11 veri kümesi kullanarak derin öğrenme ve makine öğrenmesi yöntemlerinin entegrasyonunu incelemekte ve elde edilen sonuçlar, bu yaklaşımların parazit tespitinde önemli performans artışları sağladığını göstermektedir. Yapılan deneyler sonucu en yüksek başarı ViT-FPN destekli YOLO11m modeli ile elde edilmiş; F1 skoru %99,53, mAP skoru ise %95,62 olarak ölçülmüştür. Ayrıca, derin öğrenme tabanlı yaklaşımların çıktılarının makine öğrenmesi sınıflayıcılarıyla birleştirilmesi, doğruluk oranlarını artırarak kaynak kullanımını daha verimli hale getirmiştir. Bu araştırma, parazit tespitinde yapay zeka uygulamalarının etkinliğini vurgulayarak, literatüre önemli bir katkı sunmaktadır.
Anahtar Kelimeler
bağırsak paraziti yapay zeka derin öğrenme sınıflandırma YOLO
Destekleyen Kurum
TÜBİTAK
Proje Numarası
1919B012466699
Kaynakça
- AlDahoul, N., Abdul Karim, H., Kee, S. L., & Tan, M. J. T. (2022). Localization and classification of parasitic eggs in microscopic images using an EfficientDet detector. 2022 IEEE International Conference on Image Processing (ICIP), 1922–1926. https://doi.org/10.1109/ICIP46576.2022.9897844
- AlDahoul, N., Abdul Karim, H., Momo, M. A., Escobar, F. I. F., Magallanes, V. A., & Tan, M. J. T. (2023). Parasitic egg recognition using convolution and attention network. Scientific Reports, 13, 14475. https://doi.org/10.1038/s41598-023-41711-3
- Anantrasirichai, N., Chalidabhongse, T. H., Palasuwan, D., Naruenatthanaset, K., Kobchaisawat, T., Nunthanasup, N., Boonpeng, K., Ma, X., & Achim, A. (2022). ICIP 2022 challenge on parasitic egg detection and classification in microscopic images: Dataset, methods and results. IEEE International Conference on Image Processing (ICIP). https://doi.org/10.21227/vyh8-4h71
- Belete, Y. A., Kassa, T. Y., & Baye, M. F. (2021). Prevalence of intestinal parasite infections and associated risk factors among patients of Jimma health center requested for stool examination, Jimma, Ethiopia. PLoS ONE, 16, e247063.
- Lane, M., Kashani, M., Barratt, J. L., Qvarnstrom, Y., Yabsley, M. J., Garrett, K. B., et al. (2023). Application of a universal parasite diagnostic test to biological specimens collected from animals. International Journal for Parasitology: Parasites and Wildlife, 20, 20–30.
- Pedraza, A., Ruiz-Santaquiteria, J., Deniz, O., & Bueno, G. (2022). Parasitic egg detection and classification with transformer-based architectures. 2022 IEEE International Conference on Image Processing (ICIP), 1942–1946. https://doi.org/10.1109/ICIP46576.2022.9897846
- Pinetsuksai, N., Jaksukam, K., Kittichai, V., Tongloy, T., Chuwongin, S., Jomtarak, R., & Boonsang, S. (2023). Development of self-supervised learning with Dinov2-distilled models for parasite classification in screening. 2023 15th International Conference on Information Technology and Electrical Engineering (ICITEE). https://doi.org/10.1109/ICITEE59582.2023.10317719
- Pratama, Y., Fujimura, Y., Funatomi, T., & Mukaigawa, Y. (2022). Parasitic egg detection and classification by utilizing the YOLO algorithm with deep latent space image restoration and GrabCut augmentation. 2022 IEEE International Conference on Image Processing (ICIP), 1881–1885. https://doi.org/10.1109/ICIP46576.2022.9897645
- Rajasekar, S. J. S., Jaswal, G., Perumal, V., Ravi, S., & Dutt, V. (2023). Parasite.ai – An automated parasitic egg detection model from microscopic images of fecal smears using deep learning techniques. 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI). https://doi.org/10.1109/ACCAI58221.2023.10200869
- Ruiz-Santaquiteria, J., Pedraza, A., Vallez, N., & Velasco, A. (2022). Parasitic egg detection with a deep learning ensemble. 2022 IEEE International Conference on Image Processing (ICIP), 1937–1941. https://doi.org/10.1109/ICIP46576.2022.9897858
- Suzuki, C. T., Gomes, J. F., Falcao, A. X., Papa, J. P., & Hoshino-Shimizu, S. (2012). Automatic segmentation and classification of human intestinal parasites from microscopy images. IEEE Transactions on Biomedical Engineering, 60(3), 803–812.
- Taghipour, A., Javanmard, E., Rahimi, H. M., Abdoli, A., Matin, S., Haghbin, M., et al. (2024). Prevalence of intestinal parasitic infections in patients with diabetes: A systematic review and meta-analysis. International Health, 16, 23–34.
- Tureckova, A., Turecek, T., & Oplatkova, Z. K. (2022). ICIP 2022 challenge: PEDCMI, TOOD enhanced by slicing-aided fine-tuning and inference. IEEE International Conference on Image Processing (ICIP) Proceedings. https://doi.org/10.1109/ICIP46576.2022.9897826
- World Health Organization. (2023). Soil-transmitted helminth infections. https://www.who.int/news-room/fact-sheets/detail/soil-transmitted-helminth-infections
- Xu, W., Zhai, Q., Liu, J., Xu, X., & Hua, J. (2024). A lightweight deep-learning model for parasite egg detection in microscopy images. Parasites & Vectors, 17, 454. https://doi.org/10.1186/s13071-024-06503-2
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Bilgisayar Görüşü, Görüntü İşleme |
| Bölüm | Araştırma Makalesi |
| Yazarlar | |
| Proje Numarası | 1919B012466699 |
| Gönderilme Tarihi | 13 Haziran 2025 |
| Kabul Tarihi | 7 Kasım 2025 |
| Yayımlanma Tarihi | 3 Aralık 2025 |
| Yayımlandığı Sayı | Yıl 2025 Cilt: 28 Sayı: 4 |