A comparison of two CNN-based instrument detection approaches for automated surgical assistance systems
نویسندگان
چکیده
Abstract The shortage of operating room technicians has led to a growing demand for automated systems in the OR maintain quality care. Robotic scrub nurse (RSN) are increasingly being developed, which perform tasks such as handling instruments and documenting surgery. While research focused on detecting hands surgical staff or recognizing phases, there is lack instrument tray. Therefore, this study proposes evaluates two distinct methodologies detection table using deep learning approaches YOLOv5 Mask R-CNN. performance been evaluated 18 models twelve R-CNN models, mainly differing model size. Two sets were used assess generalizability models. results show mean average precision (mAP) score 0.978 0.846 test dataset comprising three classes. An mAP 0.874 0.707 have computed respectively including six provides comparison suitable tray enhance development RSN systems.
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ژورنال
عنوان ژورنال: Current Directions in Biomedical Engineering
سال: 2023
ISSN: ['2364-5504']
DOI: https://doi.org/10.1515/cdbme-2023-1150