基于YOLOv2的微创手术机器人肺结核病灶图像识别研究
Research on Image Recognition of Tuberculosis Lesions by Minimally Invasive Surgical Robot Based on YOLOv2

肺结核是全球公共卫生问题，每年数百万人受到其影响，其中包括成千上万的死亡病例。尽管存在有效的治疗方法，但及时诊断和早期干预仍然是防止病灶扩大的关键，其中外科手术治疗成为了一种有效手段。本文提出了一种基于YOLOv2算法的微创手术机器人肺结核病灶识别技术。该技术结合了微创手术机器人、内窥镜、K210视觉识别模块以及YOLOv2识别算法，以提高肺结核病灶实时监测的识别准确度和处理效率。本文构建了训练数据集，使用YOLOv2算法进行深度网络训练，并评估了性能指标，包括准确率、召回率、精确度和F1分数。实验结果表明，该技术具有高准确度和高效率，有望改进肺结核的检测与治疗手段。
Tuberculosis has been a major challenge in the global public health sector, affecting millions of people annually, including tens of thousands of fatalities. Despite the existence of effective treatment methods, timely diagnosis and early intervention remain crucial in preventing the spread of lesions. Surgical intervention has emerged as an effective means of treatment. This paper introduces a minimally invasive surgical robot-based tuberculosis lesion recognition technology, which is built upon the YOLOv2 algorithm. This technology combines robot system design, endoscopy, the K210 visual recognition module, and the YOLOv2 recognition algorithm to enhance the identification accuracy and the handling efficiency for real-time recognition. The paper constructed a diverse training dataset, conducted deep network training using the YOLOv2 algorithm, and evaluated performance metrics, including accuracy, recall, precision, and F1 score. Experimental results demonstrate that this technology offers high accuracy and efficiency, with the potential to enhance tuberculosis detection and treatment.