Research on puncture classification method of seed implantation robot based on force/image model

Abstract

This article focuses on the use of a puncture classification method for prostate cancer seed implantation robots. To address the limitations of seed implantation surgery, different tissues in the human body are punctured and classified to improve the precision and success rate of seed implantation. This paper discusses an information fusion sensing technique that can be used in seed implantation surgery, which senses the real-time state of the puncture needle by fusing force and image modes and dramatically improves puncture accuracy and surgical tolerance. In addition, considering the multiscale feature problem of the image recognition process, this paper improves the YOLOv8 model by adding a global attention mechanism (GAM_Attention) to the backbone of the model, which significantly improves the image recognition success rate. Then, datasets of the puncture force and real-time puncture images were produced through puncture experiments. Preliminary puncture experiments with different models were completed, and the experimental results showed that the classification method improved the classification accuracy. The accuracy of seed implantation is improved by this method, which may provide a new idea for deep learning in seed implantation modality perception.