Diagnosis of Melanoma Using Differential Evolution Optimized Artificial Neural Network

Melanoma, a prevalent invasive skin cancer, is treatable in its early stages with timely diagnosis and prompt intervention. However, accurately diagnosing lesions through visual inspection or based on their characteristics remains challenging for physicians. Medical imaging techniques play a crucial role in the rapid and precise prognosis of skin lesions. Our research focuses on analyzing and classifying early-stage melanoma using machine learning techniques. In this paper, we propose a metaheuristic algorithm, Differential Evolution optimized Artificial Neural Network (DEO-ANN), for melanoma diagnosis. Color features are assessed from the Region of Interest (ROI) of the lesion using RGB and opponent color space to enhance classification accuracy. Classification is performed using an artificial neural network trained by a differential evolution algorithm. Simulated output demonstrates that the trained DEO-ANN classifier achieves an Area Under Curve (AUC) of 0.98966 with an accuracy of 94.9% on an ISIC dataset.