Deep Learning Approach for the Diagnosis of Pediatric Pneumonia Using Chest X-ray Imaging

TL;DR

This study evaluates the effectiveness of advanced CNN architectures in automatically diagnosing pediatric pneumonia from chest X-ray images, demonstrating high accuracy and sensitivity with transfer learning.

Contribution

It compares multiple CNN models for pediatric pneumonia detection, highlighting RegNet's superior performance using a curated dataset and transfer learning.

Findings

RegNet achieved 92.4% accuracy and 90.1% sensitivity.

ResNetRS achieved 91.9% accuracy and 89.3% sensitivity.

EfficientNetV2 achieved 88.5% accuracy and 88.1% sensitivity.

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).