Study on urinary erythocyte images classification based on supervised comparative learning

Qingbo Ji; Qingquan Liu; Pengfei Zhang; Tingshuo Yin; Changbo Hou

doi:10.1117/12.2671277

28 April 2023 Study on urinary erythocyte images classification based on supervised comparative learning

Qingbo Ji, Qingquan Liu, Pengfei Zhang, Tingshuo Yin, Changbo Hou

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 126101M (2023) https://doi.org/10.1117/12.2671277
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

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.

Citation Download Citation

Qingbo Ji, Qingquan Liu, Pengfei Zhang, Tingshuo Yin, and Changbo Hou "Study on urinary erythocyte images classification based on supervised comparative learning", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 126101M (28 April 2023); https://doi.org/10.1117/12.2671277

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KEYWORDS

Education and training

Machine learning

Image classification

Feature extraction

Red blood cells

Diseases and disorders

Data modeling

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