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

arXiv: https://arxiv.org/abs/2404.15129 · HTML: https://mlsanity.com/paper/2404.15129/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 23 Apr 2024.

## Builds on (most cited first, 4 of 4)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [A Review of Object Detection Models based on Convolutional Neural Network](https://mlsanity.com/paper/1905.01614/) (2019)
- [Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learning](https://mlsanity.com/paper/2204.11433/) (2022)

## Built on by (most cited first, 1 of 1)

- [Detecting Backdoors in Object Detection via Pre-NMS Prediction Distribution Shift](https://mlsanity.com/paper/2608.19088/) (2026)

## Abstract (arXiv)

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.