This paper proposes a novel method to address the challenge of imbalanced data in medical image classification, specifically for COVID-19 detection using chest X-rays. It combines a progressive generative adversarial network (ProGAN) for synthetic data generation with a Slime Mould Algorithm (SMA) optimized ResNet50V2 classifier, achieving high accuracy on a large, imbalanced dataset.