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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
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-05T22:04:36.000Z
First collected: 2026-09-20T21:32:07.623Z. This is not the publication date.