“A Weighted Classifier Approach for Pneumonia Detection in Chest X-Rays Using Transfer Learning and Synthetic Data Augmentation”

Abstract

Pneumonia remains one of the leading causes of death, particularly in children, and it affects a significant portion of the global population. Chest X-rays are the primary diagnostic tool for detecting pneumonia; however, accurate interpretation of these images is challenging, even for trained radiologists. This study presents an efficient deep learning-based model for pneumonia detection designed to assist radiologists in making more accurate diagnoses. The model utilizes transfer learning techniques with pre-trained architectures, specifically ResNet50 and MobileNetV2, to leverage the learned features for optimal performance. Generative Adversarial Networks (GANs) were employed to augment the training dataset, producing over 1 lakh synthetic chest X-ray images to improve model generalization. The final model was trained and evaluated using a combined dataset of real and synthetic data, which demonstrated high performance. The model achieved 98% test accuracy, significantly reducing computational complexity compared with other models, making it suitable for deployment in resource-constrained environments. Our findings suggest that this model can be used for rapid and reliable pneumonia diagnosis, offering a valuable tool to support healthcare professionals in clinical decision making.