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- W4367295854 abstract "The number of chronic kidney disease in China has shown a rapid upward trend. The morphological analysis of urinary erythrocyte is the key to diagnose various types of chronic kidney disease. Therefore, this paper studies the classification method of urinary erythrocyte based on supervised contrastive learning. Aiming at the low spatial resolution of urine erythrocyte and the difficulty in feature extraction, this paper proposes a Small Resolution Residual Network (SRRN) structure model based on ResNet-50 model. A multi-contrastive loss function is proposed for the singularity of feature similarity measurement in supervised contrastive learning. Based on supervised contrastive loss function, a feature similarity measurement method based on Euclidean distance is added. Aiming at the low accuracy and recall rate of some categories in urine red blood cell classification, this paper introduces a weight balance mechanism in cross entropy loss and sets higher loss weights for more difficult categories. The accuracy of this method and ResNet-50 network model on the urinary erythrocyte dataset is 92.26% and 90.7% respectively, which shows the effectiveness of this method on urinary red blood cell recognition." @default.
- W4367295854 created "2023-04-29" @default.
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- W4367295854 date "2023-04-29" @default.
- W4367295854 modified "2023-10-16" @default.
- W4367295854 title "Study on urinary erythocyte images classification based on supervised comparative learning" @default.
- W4367295854 doi "https://doi.org/10.1117/12.2671277" @default.
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