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Multi-class analysis of urinary particles based on deep learning
  • +3
  • Qiaoliang Li,
  • Zhigang Yu,
  • Suwen Qi,
  • Shiyu Li,
  • Zhuoying He,
  • Huimin Guan
Qiaoliang Li
School of Biomedical Engineering, Health Science Centre, Shenzhen University
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Zhigang Yu
School of Biomedical Engineering, Health Science Centre, Shenzhen University
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Suwen Qi
School of Biomedical Engineering, Health Science Centre, Shenzhen University
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Shiyu Li
School of Biomedical Engineering, Health Science Centre, Shenzhen University
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Zhuoying He
School of Biomedical Engineering, Health Science Centre, Shenzhen University
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Huimin Guan
School of Biomedical Engineering, Health Science Centre, Shenzhen University
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Abstract

Abstract Aim: Traditional artificial microscopic technologies cannot meet the current demands of automated urine detection. Furthermore, the number of cell types detected in previous studies was relatively limited; therefore, previous studies are considered to be insufficient. Methods: The present study proposes a multi-class detection method of urinary particles based on deep learning. First, we obtained an image database containing 15 types of cellular components, i.e., normal, shrinking, glomerular, and abnormal erythrocytes; leukocytes; calcium oxalate, uric acid, other types of crystals; particle and transparent casts; epithelial cells; low-transitional epithelium; Candida; Bacillus; and abnormal epithelium. The image data was then input into Resnet50 basic network and feature pyramid network (FPN) to obtain a multi-layer feature map. Thereafter, the classification sub-networks and regression sub-networks were used to classify and locate the cellular components. The network detection model was obtained after training was completed. Results: The experimental data showed that for the test set, the mean average precision (mAP) of the network model reached 82.86%, and the time required to process a single image sample was 195 ms. Therefore, we were able to perform multi-class analysis and detect urine cells with good results in terms of detection speed. Conclusion: This study applies the deep learning network model for the multi-category detection of urine cells. The method can be used to analyze and detect urinary particles in actual clinical practice and has great reference significance for the detection of other cells in the clinic.