用自研轻量CNN实现X光片骨折自动检测，准确率达95.96%
Fracture Detection In X-rays Using Custom Convolutional Neural Network (CNN) And Transfer Learning Models
- 设计专用轻量卷积神经网络，直接训练于骨折影像数据
- 在FracAtlas数据集上达95.96%准确率，F1-score为0.91
- 强调小样本下模型评估需考虑数据不平衡与外部验证
骨骨折是全球重大健康挑战，常导致疼痛、行动受限及生产力下降，尤其在缺乏专业放射科服务的低资源地区。传统影像方法存在成本高、辐射暴露大且依赖专家解读等问题。为此，我们开发了一种基于AI的X光片骨折自动检测方案，采用自研卷积神经网络（CNN）并对比了EfficientNetB0、MobileNetV2和ResNet50等迁移学习模型。训练使用公开的FracAtlas数据集，包含4,083张匿名骨骼放射影像。自研CNN在该数据集上取得95.96%准确率、0.94精确率、0.88召回率和0.91 F1-score。尽管迁移学习模型表现较差，但结果需结合类别不平衡和数据集局限性理解。本研究凸显轻量级CNN在骨折检测中的潜力，并强调公平基准测试、多样化数据集与外部验证对临床转化的重要性。
原文摘要 · Abstract (English)
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
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