Gallbladder Cancer Detection in Ultrasound Images based on YOLO and Faster R-CNN

TL;DR

This study compares YOLO and Faster R-CNN for gallbladder detection in ultrasound images, proposing a fusion method that improves accuracy in gallbladder cancer classification.

Contribution

It introduces a fusion approach combining YOLO and Faster R-CNN to enhance detection accuracy in medical ultrasound images.

Findings

Fusion method achieves 92.62% accuracy.

Compared to individual models, the fusion improves detection performance.

The approach enhances gallbladder cancer classification accuracy.

Abstract

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.