Advancing Melanoma Detection using Progressive Growing Generative Adversarial Network: A Novel Generative Approach

Melanoma is one of the most deadly types of skin cancer, and finding it early and correctly is very important for helping patients live longer. Traditional diagnostic methods require a lot of resources and expert interpretation, which is why automated solutions are needed. In this research, we introduce TP-GAN, a generative framework that combines Progressive Growing GAN (PGGAN) with a Student’s t-distribution, squeeze-and-excitation blocks, and dynamic residual scaling to produce varied, high-resolution melanoma images. TP-GAN makes synthetic images that are used to balance the ISIC-۲۰۲۰ dataset and teach an Xception classifier through transfer learning.  We also made a web-based diagnostic interface that lets us classify uploaded skin lesion images in real time to make it easier to use. Experimental evaluation shows that TP-GAN produces better images (FID = ۱.۰۱۲, IS = ۳.۲۴۹) and, when used with Xception, produces the best diagnostic results (accuracy = ۹۸.۲۵%, sensitivity = ۹۸.۰۲%, specificity = ۹۸.۵۲%, AUC = ۰.۹۹), beating other GAN-based methods. By uniting advanced generative modeling, deep learning classification, and user-centered design, this work provides a fast, reliable, and accessible tool for melanoma detection, with potential to support early intervention and improved clinical outcomes.

کلیدواژه ها:

Melanoma Deep Learning Generative Adversarial Networks Progressive Growing GAN Skin Lesion Classification Human ، Computer Interaction Web ، Based Diagnostic System Privacy Security