用深度学习自动判读儿童胸片，准确率达92.4%。
Deep Learning Approach for the Diagnosis of Pediatric Pneumonia Using Chest X-ray Imaging
- 基于迁移学习，用ResNetRS、RegNet等模型分类儿童胸片
- 最高准确率92.4%，敏感度90.1%，优于其他模型
- 适合医疗资源不足地区辅助诊断肺炎，提升儿科影像效率
儿童肺炎仍是全球儿童发病率和死亡率的主要原因。及时准确的诊断至关重要，但常因放射科专业人才短缺及儿童影像生理与操作复杂性而受阻。本研究评估了前沿卷积神经网络架构ResNetRS、RegNet和EfficientNetV2在迁移学习基础上对儿童胸片进行肺炎/正常二分类的性能。从原始包含5,856张儿童胸片的公开数据集中提取了1,000张图像子集，经预处理和标注后用于训练与评估。各模型均使用ImageNet预训练权重进行微调，并基于准确率与敏感度进行评价。RegNet表现最优，准确率为92.4%，敏感度为90.1%；其次为ResNetRS（准确率91.9%，敏感度89.3%）；EfficientNetV2准确率88.5%，敏感度88.1%。
原文摘要 · Abstract (English)
Pediatric pneumonia remains a leading cause of morbidity and mortality in children worldwide. Timely and accurate diagnosis is critical but often challenged by limited radiological expertise and the physiological and procedural complexity of pediatric imaging. This study investigates the performance of state-of-the-art convolutional neural network (CNN) architectures ResNetRS, RegNet, and EfficientNetV2 using transfer learning for the automated classification of pediatric chest Xray images as either pneumonia or normal.A curated subset of 1,000 chest X-ray images was extracted from a publicly available dataset originally comprising 5,856 pediatric images. All images were preprocessed and labeled for binary classification. Each model was fine-tuned using pretrained ImageNet weights and evaluated based on accuracy and sensitivity. RegNet achieved the highest classification performance with an accuracy of 92.4 and a sensitivity of 90.1, followed by ResNetRS (accuracy: 91.9, sensitivity: 89.3) and EfficientNetV2 (accuracy: 88.5, sensitivity: 88.1).
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。