Urine red blood cell classification based on Siamese Network

Abstract

Urine examination is an important examination commonly used in medical in vitro examination. The morphological classification of red blood cells in urine plays an important role in the diagnosis of hematuria and renal diseases. In order to improve the accuracy and efficiency of urine red blood cell classification, a urinary red blood cell classification algorithm based on Siamese Network of dual-mode contrastive loss function was proposed. In the dual-mode contrastive loss function, the Cosine Similarity measure and a weight factor were added as the constraint of Euclidean distance in the Contrastive Loss function, which improves the similarity judgment ability of the Contrastive Loss function for the two input eigenvectors. The experimental results show that the Cosine Similarity measure has a positive effect on the Contrastive Loss function, and can effectively improve the accuracy of the whole classification model.