Medical image analysis is a significant application of artificial intelligence for disease diagnosis. A crucial step in this process is the identification of regions of interest within the images. This task can be automated using object detection algorithms. YOLO and Faster R-CNN are renowned for such algorithms, each with its own strengths and weaknesses. This study aims to explore the advantages of both techniques to select more accurate bounding boxes for gallbladder detection from ultrasound images, thereby enhancing gallbladder cancer classification. A fusion method that leverages the benefits of both techniques is presented in this study. The proposed method demonstrated superior classification performance, with an accuracy of 92.62%, compared to the individual use of Faster R-CNN and YOLOv8, which yielded accuracies of 90.16% and 82.79%, respectively.

翻译：医学图像分析是人工智能在疾病诊断中的重要应用，其关键步骤之一在于识别图像中的感兴趣区域。这一任务可通过目标检测算法实现自动化。YOLO与Faster R-CNN是该类算法中的典型代表，两者各具优势与局限。本研究旨在探索两种技术的优势，从超声图像中筛选更准确的边界框以提升胆囊癌分类效果。本研究提出了一种融合方法，综合运用两种技术的优势。实验结果表明，与单独使用Faster R-CNN（准确率90.16%）和YOLOv8（准确率82.79%）相比，所提方法展现出更优的分类性能，准确率达到92.62%。