Kidney Stone Detection based on Improved YOLOv7 with Attention Module and Super Resolution Techniques Under Limited Training Samples

Medical imaging is considered the first step in the examination for diagnosing kidney disease. However, the recent increase in kidney stone cases has put a remarkable burden on the whole medical diagnosis system. This problem causes a demand of an automatic kidney stone detection model to decrease the weight for medical imaging phase. Moreover, X-ray imaging, which is the current most popular medical imaging technique, is struggling with false positives, which is caused by low quality, making the diagnosis more challenging. Due to the two main problems above, this study is carried out with two contributions. First, fusing different attention modules to YOLOv7 architecture that shall bring higher performance of kidney stone detection task. Second, proposing the use of super resolution (SR) models that shall address the problem of low quality in X-ray image. As a result, the proposed YOLOv7 with attention modules easily outperforms the YOLOv7 baseline in detection performance, the highest accuracy model belongs to convolution block attention module attached with YOLOv7, which reaches 91.2% mAP50. When SR models are applied to upsample X-ray images, these SR X-ray images enable the proposed attention-based models to improve the precision and sensitivity considerably, with the highest precision reaching 97.3% and highest sensitivity hitting a peak at 91.7%. Consequently, our methods are proposed to address current issues of kidney stone diagnosis and contribute another aspect of X-ray image enhancement.