An Optimized Convolutional Neural Network for Classification of Melanoma Skin Cancer

Abstract

Melanoma is the most lethal form of skin cancer, responsible for approximately 75% of all skin cancer-related deaths despite representing only 4% of all skin cancer cases. Early and accurate detection is critical to improving patient survival rates. This study proposes an optimized deep learning framework for the automated classification of melanoma and other skin lesion types using dermoscopic images from the ISIC 2019 dataset (HAM10000). Three convolutional neural network (CNN) architectures, namely ResNet50, VGG16, and VGG19, were implemented via transfer learning and fine-tuned using two metaheuristic optimization algorithms: the Genetic Algorithm (GA) and the Grey Wolf Optimizer (GWO).