Urine Sediment Detection Algorithm Based on Channel Enhancement and Deformable Convolution

Urine sediment detection is a vital method in clinical urine analysis for evaluating an individual's kidney and urinary system health, as well as identifying potential diseases. Nevertheless, urine sediment images exhibit the characteristic of diverse shapes for the same category of targets. These characteristics pose a considerable challenge to the accurate identification of the visible components within the images. We approach urine sediment detection as an object detection task and have introduced the specialized YOLOv7-CSD algorithm for this purpose. In particular, we have integrated channel enhancement feature pyramid network (CE-FPN) and selective kernel (SK) into the YOLOv7 model to address the issue of model confusion in classifying and identifying tasks caused by the feature aliasing effects of feature pyramid network (FPN). Furthermore, we enhance the efficient layer aggregation networks (ELAN) network by adding a second channel, enabling the model to acquire a more extensive set of feature information. On top of this, we introduce the deformable convolutional v3 (DCNv3) operator, allowing the model to dynamically adjust its receptive field, addressing the issue of variable shapes. Tested on the USE dataset and a dataset for urine crystals, YOLOv7-CSD achieves accuracies of 92.8 $\%$ and 89.6 $\%$ , respectively.