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.
Abstract (translated)
医学图像分析是人工智能在疾病诊断中的一个重要应用。这一过程中一个关键步骤是在图像中识别感兴趣区域。这项任务可以使用物体检测算法来自动化。YOLO和Faster R-CNN是臭名昭著的这类算法，各自具有自己的优势和劣势。本研究旨在探讨两种算法的优势，以提高从超声图像中检测胆囊结石的边界框的准确性，从而提高胆囊癌分类。本研究提出了一种融合方法，利用两种算法的优势。与单独使用Faster R-CNN和YOLOv8相比，该方法显示出卓越的分类性能，准确率为92.62%，而分别使用这两种方法时的准确率分别为90.16%和82.79%。
URL
https://arxiv.org/abs/2404.15129
