AI-Enhanced Pediatric Pneumonia Detection: A CNN-Based Approach Using Data Augmentation and Generative Adversarial Networks (GANs)

TL;DR

This paper develops a CNN-based system utilizing data augmentation and GANs to improve pediatric pneumonia detection from chest X-rays, demonstrating enhanced accuracy and real-time deployment in clinical settings.

Contribution

It introduces a novel combination of data augmentation and GAN-generated images to address data scarcity in pediatric pneumonia classification using CNNs.

Findings

Achieved high accuracy and F1 scores with combined data

Demonstrated effective real-time classification via web app

Showed potential for resource-limited clinical environments

Abstract

Pneumonia is a leading cause of mortality in children under five, requiring accurate chest X-ray diagnosis. This study presents a machine learning-based Pediatric Chest Pneumonia Classification System to assist healthcare professionals in diagnosing pneumonia from chest X-ray images. The CNN-based model was trained on 5,863 labeled chest X-ray images from children aged 0-5 years from the Guangzhou Women and Children's Medical Center. To address limited data, we applied augmentation techniques (rotation, zooming, shear, horizontal flipping) and employed GANs to generate synthetic images, addressing class imbalance. The system achieved optimal performance using combined original, augmented, and GAN-generated data, evaluated through accuracy and F1 score metrics. The final model was deployed via a Flask web application, enabling real-time classification with probability estimates. Results demonstrate the potential of deep learning and GANs in improving diagnostic accuracy and efficiency for pediatric pneumonia classification, particularly valuable in resource-limited clinical settings https://github.com/AbdulManaf12/Pediatric-Chest-Pneumonia-Classification