Abstract
Bone fractures present a major global health challenge, often resulting in pain, reduced mobility, and productivity loss, particularly in low-resource settings where access to expert radiology services is limited. Conventional imaging methods suffer from high costs, radiation exposure, and dependency on specialized interpretation. To address this, we developed an AI-based solution for automated fracture detection from X-ray images using a custom Convolutional Neural Network (CNN) and benchmarked it against transfer learning models including EfficientNetB0, MobileNetV2, and ResNet50. Training was conducted on the publicly available FracAtlas dataset, comprising 4,083 anonymized musculoskeletal radiographs. The custom CNN achieved 95.96% accuracy, 0.94 precision, 0.88 recall, and an F1-score of 0.91 on the FracAtlas dataset. Although transfer learning models (EfficientNetB0, MobileNetV2, ResNet50) performed poorly in this specific setup, these results should be interpreted in light of class imbalance and data set limitations. This work highlights the promise of lightweight CNNs for detecting fractures in X-rays and underscores the importance of fair benchmarking, diverse datasets, and external validation for clinical translation
Abstract (translated)
骨骼骨折是全球健康的一大挑战，常常导致疼痛、活动能力下降和生产力损失，尤其是在资源有限的环境中，那里获取专家放射学服务的机会较少。传统影像方法存在成本高、辐射暴露以及依赖专业解读的问题。为解决这些问题，我们开发了一种基于人工智能（AI）的方法，利用自定义卷积神经网络(CNN)从X射线图像中自动检测骨折，并将其与包括EfficientNetB0、MobileNetV2和ResNet50在内的迁移学习模型进行了对比测试。训练是在公开可用的FracAtlas数据集上进行的，该数据集包含4,083张匿名化的肌肉骨骼放射影像。自定义CNN在FracAtlas数据集上的准确率为95.96%，精确度为0.94，召回率（灵敏度）为0.88，F1分数达到0.91。尽管迁移学习模型在此特定设置中的表现不佳，但这些结果应该考虑到类别不平衡和数据集限制的影响来解读。这项工作突显了轻量级CNN在X射线骨折检测方面的潜力，并强调了公平基准测试、多样化数据集以及外部验证对于临床应用的重要性。
URL
https://arxiv.org/abs/2509.06228
