Urinary Epithelial Cell Classification Method Based on Comparative Learning and Multilevel Feature Fusion

In response to the challenges faced by current cell classification methods, including susceptibility to image quality variations and low classification accuracy due to limited labeled data, a cell classification method is proposed in this paper. First, the multi-level feature fusion module fuses semantic feature information and spatial feature information. Secondly, the adaptive comparison loss function can automatically adjust the loss weight according to the similarity between cell features, so that the model can learn more discriminative high-dimensional features and improve the classification accuracy. The experiments were conducted using 5-fold cross-validation. The results show that the urine exfoliated cell classification method proposed in this paper has 96.23% sensitivity and 97.2% specificity on theIdeepwise urine exfoliated cell dataset, which has a 2.34 percent improvement in sensitivity and 4.6 percent improvement in specificity compared with the existing methods.