# A Comparative Study of GAN-Based Deep Learning Models for Pneumonia Detection in Chest X-Rays

arXiv: https://arxiv.org/abs/2609.06276 · HTML: https://mlsanity.com/paper/2609.06276/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 5 Sep 2026.

## Builds on (most cited first, 3 of 3)

- [GAN-based Data Augmentation for Chest X-ray Classification](https://mlsanity.com/paper/2107.02970/) (2021)
- [A multi-stage GAN for multi-organ chest X-ray image generation and segmentation](https://mlsanity.com/paper/2106.05132/) (2021)
- [When CNNs Outperform Transformers and Mambas: Revisiting Deep Architectures for Dental Caries Segmentation](https://mlsanity.com/paper/2511.14860/) (2025)

## Abstract (arXiv)

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, augmented-data training accuracy reached approximately 100%, while validation accuracy remained near 50%, below the real-data validation accuracy. This experiment therefore did not demonstrate a validation-performance benefit from GAN augmentation. The classifier comparison highlights differences in accuracy and pneumonia recall, while the augmentation experiment indicates the need for further evaluation of synthetic-image quality and training settings.