Multi-Class Urinary Sediment Particles Detection Based on YOLOv7 With Attention Modules

Urine sediment analysis plays a vital role in the evaluation of kidney health. Traditional machine learning techniques approach the task of urine sediment particle detection as an image classification problem, wherein the particles are segmented based on features like edges or thresholds. However, the segmentation process for sediment particles in urine images is complex due to the inherent limitations of low contrast and weak edge characteristics. To mitigate the background noise on detection, that is, to focus more on the important part (i.e., cells), there have appeared several works that employ attention-based urinary sediment detector; however, their works did not consider the best location attention modules. This paper YOLOv7-based urinary sediment detection with attention modules. YOLOv7 is one of state-of-the-art models, and we additionally implement attention modules in the backbone of YOLOv7 so that they empower its network to enhance the feature extraction with mitigating the background noises. In experiments, we perform the proposed models on urinary sediment dataset and the results demonstrate that our proposed models outperform original YOLOv7 and a state-of-the-art urinary sediment detector in terms of recall score by 12.4% and 4.3% as well as mAP score by 7.4% and 1.6%. Our source is provided in the following link: https://github.com/Info-Sys-OU/Multi-class-Urinary-Sediment-Particles-Detection-based-on-YOLOv7-with-Attention-Modules.