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.

通过将 YOLO 和 Faster R-CNN 算法融合应用于超声图像中，本研究旨在选择更精确的包围盒以提高胆囊癌分类，实验证明该方法在分类性能上表现优越，准确率达到 92.62%，而仅使用 Faster R-CNN 和 YOLOv8 的准确率分别为 90.16% 和 82.79%。