Abstract
Böbrek hastalıkları, böbrek taşları ve diğer idrar yolu enfeksiyonlarının teşhisi ve tedavisi için idrardaki sediment parçacıkların analiz edilmesi ve incelenmesi kritik önem taşımaktadır. İdrar tahlili örneklerinin toplanması ve analizinin manuel yapılması insan gücü ve zaman gerektirdiği için beşerî hata riski taşımaktadır. Bu sorunların üstesinden gelmek için Evrişimli Sinir Ağlarına (CNNs) dayanan gelişmiş bir derin öğrenme modeli olan YOLOv11’in beş farklı varyantı ilk kez bu çalışmada idrar sediment parçacıklarının tespiti kullanılmıştır. YOLOv11’nin beş varyantı (YOLOv11n, YOLOv11s, YOLOv11m, YOLOv11l, YOLOv11x) ile mikroskobik idrar parçacıkların görüntüleri yedi kategoride (erythrocyte, leukocyte, epithelial cell, crystals, cast, mycete, epithelial nuclei) incelenmiştir. 5376 idrar sediment görüntüsünden oluşan veri kümesi için yapılan değerlendirmelerde, tüm varyantlar arasında YOLOv11s 0.5 IoU eşiğinde %89.6’lık mAP ile en yüksek değeri elde ederken, YOLOv11l ise %89.4 mAP değeri ile ikinci yüksek performansı göstermiştir. Görüntü başına tespit hızı ise YOLOv11s ve YOLOv11l için sırasıyla 6.7 ms ve 23.3 ms olarak elde edilmiştir. Bu çalışmada elde edilen sonuçlar, mikroskobik görüntülerden idrar sediment parçacıklarının tespit edilmesi için YOLOv11 modelinin uygulanabilme potansiyelini vurgulamaktadır.
References
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Abstract
The analysis and examination of urinary sediment particles are critically important for the diagnosis and treatment of kidney diseases, kidney stones, and other urinary tract infections. Since the collection and manual analysis of urine samples are labor-intensive and time-consuming processes, they carry a significant risk of human error. To address these challenges, this study proposes for the first time the use of five different variants of YOLOv11, a deep learning model based on Convolutional Neural Networks (CNNs), for the detection of urinary sediment particles. The five YOLOv11 variants (YOLOv11n, YOLOv11s, YOLOv11m, YOLOv11l, and YOLOv11x) were applied to microscopic images of urine samples across seven particle categories: erythrocyte, leukocyte, epithelial cell, crystals, cast, mycete, and epithelial nuclei. Based on evaluations conducted on a dataset comprising 5376 urinary sediment images, YOLOv11s achieved the highest performance with a mAP of 89.6% at a 0.5 IoU threshold, followed by YOLOv11l with a mAP of 89.4%. In terms of detection speed per image, YOLOv11s and YOLOv11l reached 6.7 ms and 23.3 ms, respectively. The results of this study highlight the potential applicability of the YOLOv11 model for the accurate and efficient detection of urinary sediment particles in microscopic images.
References
- Akhtar, S., Hanif, M., Rashid, A., Aurangzeb, K., Khan, E. A., Saraoglu, H. M., & Javed, K. (2024). An optimized data and model centric approach for multi-class automated urine sediment classification. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3385864
- Avci, D., Leblebicioglu, M. K., Poyraz, M., & Dogantekin, E. (2014). A new method based on adaptive discrete wavelet entropy energy and neural network classifier (ADWEENN) for recognition of urine cells from microscopic images independent of rotation and scaling. Journal of Medical Systems, 38, 1–9. https://doi.org/10.1007/s10916-014-0007-3
- Diwan, T., Anirudh, G., & Tembhurne, J. V. (2023). Object detection using YOLO: challenges, architectural successors, datasets and applications. Multimedia Tools and Applications, 82(6), 9243–9275. https://doi.org/10.1007/s11042-022-13644-y
- Franti, P., & Mariescu-Istodor, R. (2023). Soft precision and recall. Pattern Recognition Letters, 167, 115–121. https://doi.org/10.1016/j.patrec.2023.02.005
- Hao, F., Li, X., Li, M., Wu, Y., & Zheng, W. (2022). An accurate urine red blood cell detection method based on multi-focus video fusion and deep learning with application to diabetic nephropathy diagnosis. Electronics, 11(24), 4176. https://doi.org/10.3390/electronics11244176
- Ji, Q., Jiang, Y., Wu, Z., Liu, Q., & Qu, L. (2023). An image recognition method for urine sediment based on semi-supervised learning. IRBM, 44(2), 100739. https://doi.org/10.1016/j.irbm.2022.09.006
- Ji, Q., Li, X., Qu, Z., & Dai, C. (2019). Research on urine sediment images recognition based on deep learning. IEEE Access, 7, 166711–166720. https://doi.org/ 10.1109/ACCESS.2019.2953775
- Khalid, Z. M., Hawezi, R. S., & Amin, S. R. M. (2022, February). Urine sediment analysis by using convolution neural network. In 2022 8th International Engineering Conference on Sustainable Technology and Development (IEC) (pp. 173–178). IEEE. https://doi.org/10.1109/IEC54822.2022.9807482
- Khanam, R., & Hussain, M. (2024). Yolov11: An overview of the key architectural enhancements. arXiv preprint arXiv:2410.17725. https://doi.org/ 10.48550/arXiv.2410.17725
- Liang, Y., Tang, Z., Yan, M., & Liu, J. (2018a). Object detection based on deep learning for urine sediment examination. Biocybernetics and Biomedical Engineering, 38(3), 661–670. https://doi.org/10.1016/j.bbe.2018.05.004
- Liang, Y., Kang, R., Lian, C., & Mao, Y. (2018b). An end-to-end system for automatic urinary particle recognition with convolutional neural network. Journal of Medical Systems, 42(9), 165. https://doi.org/10.1007/s10916-018-1014-6
- Lyu, H., Xu, F., Jin, T., Zheng, S., Zhou, C., Cao, Y., ... & Li, D. (2023). Automated detection of multi-class urinary sediment particles: An accurate deep learning approach. Biocybernetics and Biomedical Engineering, 43(4), 672–683. https://doi.org/10.1016/j.bbe.2023.09.003
- Nagai, T., Onodera, O., & Okuda, S. (2022). Deep learning classification of urinary sediment crystals with optimal parameter tuning. Scientific Reports, 12(1), 21178. https://doi.org/10.1038/s41598-022-25385-x
- Suhail, K., & Brindha, D. (2024). Microscopic urinary particle detection by different YOLOv5 models with evolutionary genetic algorithm based hyperparameter optimization. Computers in Biology and Medicine, 169, 107895. https://doi.org/10.1016/j.compbiomed.2023.107895
- Suhail, K., & Brindha, D. (2021). A review on various methods for recognition of urine particles using digital microscopic images of urine sediments. Biomedical Signal Processing and Control, 68, 102806. https://doi.org/10.1016/j.bspc.2021.102806
- Sun, Q., Yang, S., Sun, C., & Yang, W. (2019). Exploiting aggregate channel features for urine sediment detection. Multimedia Tools and Applications, 78, 23883–23895. https://doi.org/10.1007/s11042-018-6241-9
- Yan, M., Liu, Q., Yin, Z., Wang, D., & Liang, Y. (2020). A bidirectional context propagation network for urine sediment particle detection in microscopic images. In ICASSP 2020 – 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 981–985). IEEE. https://doi.org/10.1109/ICASSP40776.2020.9054367
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Artificial Intelligence (Other) |
| Journal Section | Research Article |
| Authors | |
| Publication Date | December 3, 2025 |
| Submission Date | May 3, 2025 |
| Acceptance Date | October 10, 2025 |
| Published in Issue | Year 2025 Volume: 28 Issue: 4 |
