Pneumonia App: a mobile application for efficient pediatric pneumonia
  diagnosis using explainable convolutional neural networks (CNN)
Jiaming Deng Zhenglin Chen Minjiang Chen Lulu Xu Jiaqi Yang Zhendong Luo Peiwu Qin
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.