Using Synthetic Images to help classifying the Melanoma Better: A progressive growing GAN approach for adding to dermatological data

Abstract

Melanoma is a dangerous form of skin cancer, and when diagnosed early it should be treated. Melanocytic lesions are diagnosed using dermoscopy images, but manual examination may take a lot of time and is not very accurate. This present work focuses on the use of deep learning algorithms in improving the detection of melanoma from dermoscopic image. Deep learning allows the power to learn autonomously from data and make changes according to new trends to screen for melanoma cases ahead of time. The findings of this paper involve a comparison of different deep learning models, PGGAN, ResNet-50, DCGAN, and VAE in the diagnosis of melanoma. The experiment proves that such models can go even further and increase the melanoma detection precision while offering approaches that are both easily scalable and flexible for problems concerning skin cancer identification. This has delivered great results in attempting to integration of deep learning organization into conventional procedures in clinical practice to supply strong backing to dermatologists which helps them to identify melanoma cases satisfactorily