In this study, Golkarieh et al. seeks to address several shortcomings of the melanoma detecting semi-supervised generative adversarial network (SS-GAN) by incorporating four key enhancements. They added reconstruction loss to mitigate mode collapse, self-attention elements to capture global context, consistency regularization to improve discriminator outputs, and confidence-based pseudo-labeling for better use of unlabeled data. These improvements were optimized by a mutual learning-based artificial bee colony algorithm with Random Key. The enhanced model was successful in distinguishing between melanoma and non-melanoma images, with F-values in the 90% range. Moreover, the SS-GAN outperformed machine and other semi-supervised learning models, increasing the F-values by 5% and 14-18% respectively, when tested against datasets like DermNet. The study strongly suggests that enhanced SS-GAN models will evolve to be more accurate than traditional models used.