Abstract
References
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Abstract
Urine sediment tests are important in the diagnosis of abnormal diseases related to the urinary tract. The formation of cells such as red blood cells and white blood cells in the urine of patients is important for the diagnosis of the disease. Therefore, cells need to be fully identified in clinical urinalysis. Urinalysis with human eyes; Since it is subjective, time consuming and causing errors, methods have been developed to automate microscopic analysis with the help of computer and software systems. In this study, the YOLO-v7 algorithm, which gives successful results in image processing technology, was used as a method and model. The dataset used in the study was created by using microscopic images of urine sediment taken from the Biochemistry Laboratory of the Faculty of Medicine, Selcuk University. Seven different cell segmentation and classification studies have been carried out, including WBC, RBC, WBCC, Epithelial, Flat Epithelial, Mucs and Bubbles, which have clinical value for the diagnosis of the disease. Experimental studies were carried out with the YOLO-v7 algorithm and the results were presented. The contributions of this study can be summarized as follows. (1) In this study, which is proposed for segmentation of cells on the urine cell images in the Urine Sediment dataset, for the experimental studies carried out with the YOLO model, whose performance was evaluated; Precision, Recall, mAP(0.5) and F1-Score(%) segmentation performance metrics were calculated as 0.384, 0.759, 0.432 and 0.510, respectively. (2) A computer-aided support system to assist physicians in segmenting urine cells is presented as a secondary tool. Classification accuracy for WBC, RBC, WBCC, Epithelial, Flat Epithelial, Mucs and Bubbles cells was calculated as 0.78, 0.94, 0.90, 0.57, 0.92, 0.68 and 0.97, respectively. A mean classification success of 0.822 was achieved for all classes. Thus, it has been seen that the Yolov7 model can be used by experts as a tool for recognizing cells in the urine sediment. As a result, it has been shown that suitable deep learning models can be used to recognize the biometric properties of urinary sediment cells. With the model created using deep learning libraries, urine sediment cells can be easily classified, and it is possible to define many different cells if there is a dataset with sufficient number of images.
References
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- [2] W. Tangsuksant et al., “Development algorithm to count blood cells in urine sediment using ANN and Hough Transform.” The 6th 2013 Biomedical Engineering International Conference. IEEE, 2013.
- [3] M. D. Almadhoun and Alaa El-Halees. “Automated recognition of urinary microscopic solid particles.” Journal of medical engineering & technology 38.2 (2014): 104-110.
- [4] Y. Dong et al., “Evaluations of Deep Convolutional Neural Networks for Automatic Identification of Malaria Infected Cells”, 2017 IEEE EMBS international conference on biomedical & health informatics (BHI). IEEE, 2017.
- [5] Y. Liang et al., "Object detection based on deep learning for urine sediment examination.” Biocybernetics and Biomedical Engineering 38.3 (2018): 661-670.
- [6] Q. Li et al., “A recognition method of urine cast based on deep learning.” 2019 International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2019.
- [7] J.S. Velasco, M.K. Cabatuan, and E.P. Dadios, “Urine sediment classification using deep learning.” Lecture Notes on Advanced Research in Electrical and Electronic Engineering Technology (2019): 180-185.
- [8] Colab Pro, Available: https://colab.research.google.com/. [Accessed: 21.11.2022].
- [9] J. Redmon et al., “You only look once: Unified, real-time object detection.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
- [10] P. F. Felzenszwalb et al., “Object detection with discriminatively trained part-based models.” IEEE transactions on pattern analysis and machine intelligence 32.9 (2009): 1627-1645.
- [11] Chien-Yao Wang et al., “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors.” arXiv preprint arXiv:2207.02696 (2022).
- [12] S. Ren et al., “Faster r-cnn: Towards real-time object detection with region proposal networks.” Advances in neural information processing systems 28 (2015).
- [13] COCO Dataset. Available: https://cocodataset.org/#home, [Accessed: 20.05.2022].
- [14] P. Skalski, “Make Sense,” 2019. Available: https://github.com/SkalskiP/make-sense/. [Accessed: 20.05.2022].
- [15] Lökosit, Available: https://www.medicalpark.com.tr/lokosit/hg-2070. [Accessed: 21.11.2022].
- [16] İdrarda RBC, Available: https://www.acibadem.com.tr/ilgi-alani/eritrosit-rbc/#genel-tanitim. [Accessed: 21.11.2022].
- [17] İdrarda Epitel Nedir? Değeri Kaç Olmalı? Nedenleri ve Tedavisi, Available : https://saglik.li/idrarda-epitel-nedir/, [Accessed: 21.11.2022].
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Details
| Primary Language | English |
|---|---|
| Subjects | Artificial Intelligence |
| Journal Section | Articles |
| Authors | |
| Early Pub Date | April 28, 2023 |
| Publication Date | April 30, 2023 |
| Submission Date | January 17, 2023 |
| Acceptance Date | March 17, 2023 |
| Published in Issue | Year 2023 |
The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License