Gallbladder Cancer Detection in Ultrasound Images based on YOLO and Faster R-CNN
This work addresses gallbladder cancer diagnosis for medical imaging, but it is incremental as it combines existing object detection techniques without introducing a fundamentally new approach.
The study tackled gallbladder cancer detection in ultrasound images by fusing YOLO and Faster R-CNN to improve bounding box accuracy, resulting in a classification accuracy of 92.62%, outperforming individual methods at 90.16% and 82.79%.
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.