Study on Fine-Grained Visual Classification of Low-Resolution Urinary Erythrocyte

This article aims to improve the classification accuracy of low-resolution urine red blood cells. This paper proposes a super-resolution method based on category-aware loss and an RBC-MIX data enhancement approach. It optimizes the cross-entropy loss to maximize the classification boundary and improve intra-class tightness and inter-class difference, achieving fine-grained classification of low-resolution urine red blood cells. Experimental outcomes demonstrate that with this method, an accuracy rate of 97.8% can be achieved for low-resolution urine red blood cell images. This algorithm attains outstanding classification performance for low-resolution urine red blood cells with only category labels required. This method can serve as a practical reference for urine r ed blood cell morphology examination items.Graphical Abstract
Source: Journal of Digital Imaging - Category: Radiology Source Type: research