A deep learning based fracture detection in arm bone X-ray images

Hoai Phuong Nguyen, T. Hoang, Huy Hoang Nguyen
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引用次数: 8

Abstract

A large number of arm fracture-related injuries are reported in hospitals and clinics around the world. In this paper, we propose a novel deep learning based fracture detection in arm bone X-ray images. First, we preprocess the Xray image by using an algorithm that is a combination of the YOLACT++ for image segmentation and Contrast Limited Adaptive Histogram Equalization for image contrast enhancement. Then, YOLOv4 is trained on a small dataset with four data augmentation techniques to identify and locate the position of bone fracture on X-ray images. The topmost result obtained is 81.91% by using our proposed method. Experimental results also confirm that our method outperforms the Faster-RCNN based solution while implementing on the small dataset.
基于深度学习的臂骨x射线图像骨折检测
世界各地的医院和诊所报告了大量与手臂骨折相关的损伤。在本文中,我们提出了一种新的基于深度学习的手臂骨x射线图像骨折检测方法。首先,我们使用一种结合了yolact++的图像分割算法和对比度有限自适应直方图均衡化算法的图像对比度增强算法对x射线图像进行预处理。然后,使用四种数据增强技术对YOLOv4进行小数据集训练,在x射线图像上识别和定位骨折位置。采用该方法得到的最优解为81.91%。实验结果也证实了我们的方法在小数据集上实现时优于基于Faster-RCNN的解决方案。
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