Abstract
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).
Abstract (translated)
儿科肺炎仍然是全球儿童发病率和死亡率的主要原因。及时且准确的诊断至关重要，但往往由于放射学专业知识有限以及儿科影像生理和操作复杂性而受到挑战。本研究调查了使用迁移学习的最先进的卷积神经网络（CNN）架构ResNetRS、RegNet和EfficientNetV2在自动分类儿童胸部X光图像为肺炎或正常情况下的性能。 从一个包含5,856张儿科影像数据集的公开可用数据集中，提取并精选出1,000张胸部X光片进行研究。所有图像均经过预处理，并进行了二元分类标签标注（即肺炎或正常）。每种模型使用预训练的ImageNet权重微调，并基于准确率和灵敏度进行评估。 结果显示，RegNet达到了最高的分类性能，其准确率为92.4%，灵敏度为90.1%；其次为ResNetRS，准确率为91.9%，灵敏度为89.3%；EfficientNetV2则分别为88.5%和88.1%。
URL
https://arxiv.org/abs/2601.00041
