Mycoplasma pneumoniae pneumonia (MPP) poses significant diagnostic challenges in pediatric healthcare, especially in regions like China where it's prevalent. We introduce PneumoniaAPP, a mobile application leveraging deep learning techniques for rapid MPP detection. Our approach capitalizes on convolutional neural networks (CNNs) trained on a comprehensive dataset comprising 3345 chest X-ray (CXR) images, which includes 833 CXR images revealing MPP and additionally augmented with samples from a public dataset. The CNN model achieved an accuracy of 88.20% and an AUROC of 0.9218 across all classes, with a specific accuracy of 97.64% for the mycoplasma class, as demonstrated on the testing dataset. Furthermore, we integrated explainability techniques into PneumoniaAPP to aid respiratory physicians in lung opacity localization. Our contribution extends beyond existing research by targeting pediatric MPP, emphasizing the age group of 0-12 years, and prioritizing deployment on mobile devices. This work signifies a significant advancement in pediatric pneumonia diagnosis, offering a reliable and accessible tool to alleviate diagnostic burdens in healthcare settings.

通过使用深度学习技术的移动应用程序PneumoniaAPP，我们在中国等高发地区解决了儿科医疗中诊断儿童肺炎的挑战，该应用程序利用了卷积神经网络(CNNs)对包含3345张胸部X光(CXR)图像的综合数据集进行训练，并在公共数据集的基础上进行了补充样本，其中包括了833张显示Mycoplasma pneumoniae pneumonia (MPP)的CXR图像。我们的CNN模型在所有类别上实现了88.20%的准确率和0.9218的AUROC，其中对于支原体类别的特定准确率达到了97.64%，并通过在PneumoniaAPP中集成可解释性技术来帮助呼吸科医生定位肺不透明度。我们的研究针对0-12岁的儿科MPP，优先部署在移动设备上，为儿科肺炎的诊断提供了可靠和易于获得的工具，显著推进了儿科肺炎的诊断方法，减轻了医疗诊断的负担。

肺炎应用：利用可解释的卷积神经网络（CNN）进行高效儿童肺炎诊断的移动应用