Abstract
Early and accurate detection of the bone fracture is paramount to initiating treatment as early as possible and avoiding any delay in patient treatment and outcomes. Interpretation of X-ray image is a time consuming and error prone task, especially when resources for such interpretation are limited by lack of radiology expertise. Additionally, deep learning approaches used currently, typically suffer from misclassifications and lack interpretable explanations to clinical use. In order to overcome these challenges, we propose an automated framework of bone fracture detection using a VGG-19 model modified to our needs. It incorporates sophisticated preprocessing techniques that include Contrast Limited Adaptive Histogram Equalization (CLAHE), Otsu's thresholding, and Canny edge detection, among others, to enhance image clarity as well as to facilitate the feature extraction. Therefore, we use Grad-CAM, an Explainable AI method that can generate visual heatmaps of the model's decision making process, as a type of model interpretability, for clinicians to understand the model's decision making process. It encourages trust and helps in further clinical validation. It is deployed in a real time web application, where healthcare professionals can upload X-ray images and get the diagnostic feedback within 0.5 seconds. The performance of our modified VGG-19 model attains 99.78\% classification accuracy and AUC score of 1.00, making it exceptionally good. The framework provides a reliable, fast, and interpretable solution for bone fracture detection that reasons more efficiently for diagnoses and better patient care.
Abstract (translated)
早期且准确地检测骨骼骨折对于尽早启动治疗以及避免延误患者的治疗结果至关重要。解读X射线图像是一项耗时且容易出错的任务，尤其是在放射学专业知识资源有限的情况下更是如此。此外，目前使用的深度学习方法通常存在误分类问题，并且缺乏可解释性来支持临床应用。为了解决这些挑战，我们提出了一种使用修改后的VGG-19模型进行骨骼骨折自动检测的框架。该框架采用了包括对比限制自适应直方图均衡（CLAHE）、Otsu阈值处理和Canny边缘检测在内的复杂预处理技术，以增强图像清晰度并帮助特征提取。因此，我们采用了一种可解释的人工智能方法——Grad-CAM来生成模型决策过程的可视化热力图，作为提高模型可解释性的手段，以便临床医生理解模型的决策流程。这种方法鼓励信任，并有助于进一步的临床验证。该框架部署在实时网络应用中，医疗专业人员可以上传X射线图像并在0.5秒内获得诊断反馈。 我们修改后的VGG-19模型的性能达到了99.78%的分类准确率和AUC分数为1.00，表现非常优秀。该框架提供了一种可靠、快速且可解释的解决方案，用于骨骼骨折检测，并能够更有效地进行诊断并改善患者的护理质量。
URL
https://arxiv.org/abs/2508.03739
