Abstract
In machine vision, detecting and locating object have always been challenging tasks mostly in medical imaging due to the lacking of data and unique feature of its image. Several studies applied CNN-based models for detecting task but the methods were complex or impracticable to real time tasks while others used YOLO models which were lighter but poor performance. In this study, we propose Super Resolution techniques included EDSR, FSRCNN, LapSRN, ESPCN, ESRGAN and GFPGAN with the proposed architecture of YOLOv7 for kidney stone detection on KUB X-ray image. As a result, our proposed YOLOv7 outperformed the YOLOv7 base version with sensitivity 87.6%, Precision 92.2%, F1 Score 89.8%, mAP50 91.2% and each of Super Resolution methods enabled the model precision and sensitivity to be improved considerably with highest precision reached 97.3% and sensitivity achieved 91.7% on upscaled images compared to the non-upscaled images. Consequently, the re-designed YOLOv7 and Super Resolution methods are proposed to address the problem of detection in diagnosis kidney stone disease.
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Nguyen, M.T.P., Le, V.T., Duong, H.T., Hoang, V.T. (2023). Detection of Kidney Stone Based on Super Resolution Techniques and YOLOv7 Under Limited Training Samples. In: Dao, NN., Thinh, T.N., Nguyen, N.T. (eds) Intelligence of Things: Technologies and Applications. ICIT 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 188. Springer, Cham. https://doi.org/10.1007/978-3-031-46749-3_3
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