Publications

A kind of cancer that occurs in skin cells is called skin cancer. The body’s most significant cancer is skin cancer (Skin Cancer (Including Melanoma)-Patient Version). The two main layers of the skin are the dermis (the lower or inner layer) and the epidermis (the higher or outer layer) (Donaldson, 2022). The most typical cancer in the world is skin cancer, which is becoming more frequent (Shao et al., 2017). The three types of cancers are basal cell carcinoma, squamous cell carcinoma, and melanoma (Skin Cancer, 2006), the primary kinds of skin cancer. Although skin cancer can occur in other parts of the body (Skin Cancer- Symptoms and Causes- Mayo Clinic, 2022), these tumors most frequently affect the face, ears, arms, and hands. We train with the Convolutional Neural Network (CNN) model to recognize skin cancer and its variations. One such model is a type of neural network called Generative Adversarial Networks (GANs), intended to generate realistic synthetic data. In this study, we provide a novel GAN architecture for image generation. The foundation of a GAN is a generator and a discriminator neural network. Although the generator generates synthetic data from random noise, the discriminator uses a training dataset to assess if the created data is bogus or real. Lack of enough medical data is one of the significant obstacles to developing and training a CNN for skin cancer classification. The quantity of training data can substantially impact the network’s performance. This project’s main objective is to [6] improve classification accuracy and precision by producing high-quality, diverse skin cancer images for CNN to train on.