A Comparative Study of GAN-Based Deep Learning Models for Pneumonia Detection in Chest X-Rays
This study evaluates pneumonia classification in chest X-rays using VGG19, MobileNetV2, ResNet50, and a custom CNN, and explores Generative Adversarial Network (GAN)-based synthetic data augmentation. MobileNetV2 achieved the highest reported accuracy of 88% with balanced class-wise performance. The custom CNN achieved pneumonia recall of 92.67% and precision of 79.43%, highlighting a precision-recall trade-off. Accuracy, F1-score, precision, recall, confusion matrices, and training curves were used to assess performance. Synthetic pneumonia images were combined with real images to investigate whether augmentation could improve classification performance. In the reported VGG19 comparison,...
| Authors | Roshan Paudel, Aashish Ghimire, Pramod Acharya |
|---|---|
| Primary category | cs.CV |
| Published | 5 September 2026 |
| Last updated at source | 5 September 2026 |
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