Enhanced Intraocular Melanoma Detection via Augmented Image Generation and DCGAN Classification

The proposed study offers a novel approach to identifying cerebral melanoma through the process of picture capture and evaluation employing Deep Convolutional neural networks Generative Adversarial Networks (DCGANs). The increasing number of cases of melanoma in the inside eye calls for more sophisticated and novel diagnostic techniques. We overcome the lack of health information by employing DCGANs to create highly accurate pictures that contribute to the expansion and variety of the training dataset. The dataset makes trained algorithms even more adept at identifying cells with melanoma. Because Python was incorporated into Windows 10 in this fashion, it was extremely flexible and efficient. This shows that the potential of GAN in biological image processing, offering a way to improve diagnostic precision and the accuracy is 99.9%. The original code and other project information are provided in a manner that makes them reproducible and enables more study to be done.