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Deep Multi-Modal Fusion Model for Identification of Eight Different Particles in Urinary Sediment

Open Access
|Aug 2024

References

  1. X. Zhang, G. Chen, K. Saruta, and Y. Terata, “Detection and classification of RBCs and WBCs in urine analysis with deep network,” in ACHI 2018: The Eleventh International Conference on Advances in Computer-Human Interactions, 2018, pp. 194–198. [Online]. Available: https://personales.upv.es/thinkmind/dl/conferences/achi/achi_2018/achi_ 2018_10_30_20157.pdf
  2. R. Kang, Y. Liang, C. Lian, and Y. Mao, “CNN-based automatic urinary particles recognition”, arXiv:1803.02699v1, 2018. [Online]. Available: https://www.academia.edu/83267226/CNN_Based_Automatic_Urinary_Particles_Recognition
  3. T. Batur, E. Çokluk, S. Akyüz, B. Uçar, H.H. Alp, and Z. Huyut, “Diagnostic performance evaluation of complete urinalysis in the diagnosis of urinary tract infection: Complete urinalysis in the diagnosis of urinary tract infection,” Chronicles of Precision Medical Researchers, vol. 3, no. 2, pp. 52–56, 2022. https://doi.org/10.5281/zenodo.6965805
  4. F. D. İnce, H. Y. Ellidağ, M. Koseoğlu, N. Şimşek, H. Yalçın, and M. O. Zengin, “The comparison of automated urine analyzers with manual microscopic examination for urinalysis automated urine analyzers and manual urinalysis,” Pract. Lab. Med., vol. 1, no. 5, pp. 14–20, Aug. 2016. https://doi.org/10.1016/j.plabm.2016.03.002
  5. N. Sharda, O. Bakhtar, B. Thajudeen, E. Meister, and H. Szerlip, “Manual urine microscopy versus automated urine analyzer microscopy in patients with acute kidney injury,” Laboratory Medicine, vol. 45, no. 4, pp. e152– e155, Nov. 2014. https://doi.org/10.1309/LMVJK6W4KQL1ZHKS
  6. F. Dong, Y. Yao, Y. Chen, Y. Guo, C. Jing, and J. Wu “Diagnostic performance of urine analysis based on flow microimaging and artificial intelligence recognition technology in suspected urinary tract infection patients,” Scandinavian Journal of Clinical and Laboratory Investigation, vol. 82, no. 5, pp. 385–390, Jul. 2022. https://doi.org/10.1080/00365513.2022.2100273
  7. J. Pan, C. Jiang, and T. Zhu, “Classification of urine sediment based on convolution neural network,” AIP Conference Proceedings, vol. 1955, no. 1, Apr. 2018, Art. no. 040176. https://doi.org/10.1063/1.5033840
  8. Q. Ji, X. Li, Z. Qu, and C. Dai, “Research on urine sediment images recognition based on deep learning,” IEEE Access, vol. 7, pp. 166711– 166720, Nov. 2019. https://doi.org/10.1109/ACCESS.2019.2953775
  9. Y. Liang, Z. Tang, M. Yan, and J. Liu, “Object detection based on deep learning for urine sediment examination,” Biocybernetics and Biomedical Engineering, vol. 38, no. 3, pp. 661–670, 2018. https://doi.org/10.1016/j.bbe.2018.05.004
  10. S. Mondal, S. Park, T. H. Vo, J. Choi, V.H.M. Doan, D.T. Phan, C.-S. Kim, B.-il. Lee, and J. Oh, “Smart inexpensive quantitative urine glucose and contaminant bromide ion sensor based on metal nanoparticles with deep learning approach,” Materials Chemistry and Physics, vol. 287, Aug. 2022, Art. no. 126289. https://doi.org/10.1016/j.matchemphys.2022.126289
  11. K. Suhail and D. Brindha, “A review on various methods for recognition of urine particles using digital microscopic images of urine sediments,” Biomedical Signal Processing and Control, vol. 68, Jul. 2021, Art. no. 102806. https://doi.org/10.1016/j.bspc.2021.102806
  12. X. Liu and Z. Sun, “A kind of computer microscopic urinary sediments analyzer by SVM,” in 2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing, Shanghai, China, pp. 483–486, Dec. 2008. https://doi.org/10.1109/ETTandGRS.2008.235
  13. M. -l. Shen and R. Zhang, “Urine sediment recognition method based on SVM and AdaBoost,” in International Conference on Computational Intelligence and Software Engineering, Wuhan, China, Dec. 2009, pp. 1–4. https://doi.org/10.1109/CISE.2009.5365881
  14. X. Zhou, X. Xiao, and C. Ma, “A study of automatic recognition and counting system of urine-sediment visual components,” in 2010 3rd International Conference on Biomedical Engineering and Informatics, Yantai, China, Oct. 2010, pp. 78–81. https://doi.org/10.1109/BMEI.2010.5639648
  15. W. Tangsuksant, C. Pintavirooj, S. Taertulakarn, and S. Daochai, “Development algorithm to count blood cells in urine sediment using ANN and Hough Transform,” in The 6th 2013 Biomedical Engineering International Conference, Amphur Muang, Thailand, Oct. 2013, pp. 1–4. https://doi.org/10.1109/BMEiCon.2013.6687725
  16. D. Avci, M.K. Leblebicioglu, M. Poyraz, and E. Dogantekin, “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,” J. Med. Syst., vol. 38, no. 7, Feb. 2014. https://doi.org/10.1007/s10916-014-0007-3
  17. C. Li, Y.Y. Tang, H. Luo, and X. Zheng, “Join Gabor and scattering transform for urine sediment particle texture analysis,” in 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF), Gdynia, Poland, Jun. 2015, pp. 410–415. https://doi.org/10.1109/CYBConf.2015.7175969
  18. Q. Sun, S. Yang, C. Sun, and W. Yang, “An automatic method for red blood cells detection in urine sediment micrograph,” in 2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC), Nanjing, China, May 2018, pp. 241–245. https://doi.org/10.1109/YAC.2018.8406379
  19. X. Jiang, F. Chen, Q. Chen, M. Si, and W. Wang, “Texture segmentation of urinary sediment image based on a weighted Gaussian mixture model with Markov random fields,” in Proceedings of the 2018 7th International Conference on Bioinformatics and Biomedical Science (ICBBS '18), New York, NY, USA, Jun. 2018, pp. 82–87. https://doi.org/10.1145/3239264.3239276
  20. T. H. N. Le, Y. Zheng, C. Zhu, K. Luu, and M. Savvides, “Multiple scale faster-RCNN approach to driver’s cell-phone usage and hands on steering wheel detection,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Las Vegas, NV, USA, 2016, pp. 46–53.
  21. A. Shrivastava, A. Gupta, and R. Girshick, “Training region-based object detectors with online hard example mining,” in Proceedings of the IEEE International Conference on Computer Vision, Jun. 2016, pp. 1440–1448. https://doi.org/10.1109/CVPR.2016.89
  22. K. Suhail and D. Brindha, “Microscopic urinary particle detection by different YOLOv5 models with evolutionary genetic algorithm based hyperparameter optimization,” Computers in Biology and Medicine, vol. 169, Feb. 2024, Art. no. 107895. https://doi.org/10.1016/j.compbiomed.2023.107895
  23. A. Çınar, M. Erkuş, T. Tuncer, H. Ayyıldız, and S.A. Tuncer, “YOLOv5 based detector for eight different urine particles components on single board computer,” International Journal of Imaging Systems and Technology, vol. 34, no. 1, Jan. 2024, Art. no. e22968. https://doi.org/10.1002/ima.22968
  24. X. Zhang, G. Chen, K. Saruta, and Y. Terata, “Detection and classification of RBCs and WBCs in urine analysis with deep network,” in The Eleventh International Conference on Advances in Computer-Human Interactions, 2018, pp. 194–198.
  25. T. Li, D. Jin, C. Du, X. Cao, H. Chen, J. Yan, N. Chen, Z. Chen, Z. Feng, and S. Liu, “The image-based analysis and classification of urine sediments using a LeNet-5 neural network,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging\Visualization, vol. 8, no. 1, pp. 109–114, 2020. https://doi.org/10.1080/21681163.2019.1608307
  26. T. Nagai, O. Onodera, and S. Okuda, “Deep learning classification of urinary sediment crystals with optimal parameter tuning,” Sci. Rep., vol. 12, Dec. 2022, Art. no. 21178. https://doi.org/10.1038/s41598-022-25385-x
  27. Q. Ji, Y. Jiang, Z. Wu, Q. Liu, and L. Qu, “An image recognition method for urine sediment based on semi-supervised learning,” IRBM, vol. 44, no. 2, Apr. 2022, Art. no. 100739. https://doi.org/10.1016/j.irbm.2022.09.006
  28. W. Liu, W. Li, and W. Gong, “Ensemble of fine-tuned convolutional neural networks for urine sediment microscopic image classification,” IET Computer Vision, vol. 14, no. 1, pp. 18–25, Feb. 2020. https://doi.org/10.1049/iet-cvi.2018.5829
  29. H. Kaiming, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778. [Online]. Available: https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/He_Deep_Residu al_Learning_CVPR_2016_paper.pdf
  30. Ç. Danacı and S.A. Tuncer, “Incorporating feature selection methods into machine learning-based Covid-19 diagnosis,” Applied Computer Systems, vol. 27, no. 1, pp. 13–18, Jun. 2022. https://doi.org/10.2478/acss-2022-0002
  31. C. Ding and H. Peng, “Minimum redundancy feature selection from microarray gene expression data,” Journal of Bioinformatics and Computational Biology, vol. 3, no. 2, pp. 185–205, 2005. https://doi.org/10.1142/S0219720005001004
  32. W. Yang, K. Wang, and W. Zuo, “Neighborhood component feature selection for high-dimensional data,” Journal of Computers, vol. 7, no. 1, pp. 161–168, Jan. 2012.
  33. M. Robnik-Sikonja and I. Kononenko, “Theoretical and empirical analysis of ReliefF and RReliefF,” Machine Learning, vol. 53, pp. 23–69, Oct. 2003. https://doi.org/10.1023/A:1025667309714
DOI: https://doi.org/10.2478/acss-2024-0005 | Journal eISSN: 2255-8691 | Journal ISSN: 2255-8683
Language: English
Page range: 35 - 44
Submitted on: Mar 28, 2024
Accepted on: Jul 12, 2024
Published on: Aug 15, 2024
Published by: Riga Technical University
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year

© 2024 Seda Arslan Tuncer, Ahmet Çınar, Merve Erkuş, Taner Tuncer, published by Riga Technical University
This work is licensed under the Creative Commons Attribution 4.0 License.