Enhancing Dermatological Diagnostics: A GAN-Based Approach for Synthetic Skin Lesion Image Generation

This paper presents a Generative Adversarial Network (GAN)-based approach for enhancing dermatological diagnostics by generating high-quality synthetic images of skin lesions. Utilizing a Deep Convolutional GAN (DCGAN) architecture, the model will be trained on the HAM10000 dataset, which contains over 10,000 dermatoscopic images of various pigmented skin lesions. The proposed model aims to address the challenge of limited data in training deep learning models by augmenting the dataset with synthetic images. Key features include the use of Spectral Normalization and Label Smoothing. The model’s performance will be evaluated using both objective metrics, such as Frechet Inception Distance (FID), Mean Deviation Similarity Index (MDSI), Structural Similarity (SSMI), and Haar Perceptual Similarity Index (HaarPSI), as well as subjective assessments by dermatologists. The goal is to integrate the generated synthetic images into real-world datasets to improve the accuracy of automated skin lesion classification, thereby enhancing early detection and diagnosis of skin cancers while reducing misdiagnosis.