Abstract
This paper presents a novel application of the YOLOv7 deep learning algorithm for multiple surgical tool localization and detection in the laparoscopic video data. This technique has been more accurate than previously due to being built via a combination of object localization and detection techniques, which enables more precise results than traditional methods. The techniques were verified using an open dataset. The experiment proves that the YOLOv7 algorithm can accurately identify the locations of numerous surgery tools throughout laparoscopy video, switching its potential as an effective tool for medical professionals working with laparoscopic video data. Consequently, the work provides valuable insights into the adoption of deep learning for diagnostic image analysis and computer vision application including effectively applied to a real-world problem such as surgical tool recognition in endoscopy videos.
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Acknowledgments
This work was financially supported by Sichuan Science and Technology Program (No. 2020YFS0454, No. 2020YFS0318), NHC Key Laboratory of Nuclear Technology Medical Transformation (MIANYANG CENTRAL HOSPITAL) (Grant No. 2021HYX031).
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Ahmed, M.F., He, G. (2024). YOLOv7-Based Multiple Surgical Tool Localization and Detection in Laparoscopic Videos. In: Wang, G., Yao, D., Gu, Z., Peng, Y., Tong, S., Liu, C. (eds) 12th Asian-Pacific Conference on Medical and Biological Engineering. APCMBE 2023. IFMBE Proceedings, vol 104. Springer, Cham. https://doi.org/10.1007/978-3-031-51485-2_6
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DOI: https://doi.org/10.1007/978-3-031-51485-2_6
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