Exploring Advanced Deep Learning Techniques for Multi-Class Melanoma Cancer Classification

Melanoma represents one of the most lethal forms of skin cancer, underscoring the importance of early detection for effective treatment and improved survival rates. Traditional diagnostic methods, which predominantly rely on visual inspection and biopsies, are often time-consuming and susceptible to human error. Timely diagnosis significantly enhances the likelihood of recovery and can reduce healthcare costs by minimizing the necessity for surgical, radiographic, or chemical treatments. Recently, deep learning techniques have demonstrated considerable promise in automating and improving the accuracy of medical diagnoses, including melanoma classification. In this study, we evaluate the performance of several state-of-the-art deep learning models—DenseNet, ResNet, VGG-16, VGG-19, Inception v3, and AlexNet—for multi-class melanoma cancer classification. Our objective is to identify the model that offers the best performance in terms of accuracy, sensitivity, and specificity. We conduct a comprehensive comparison using publicly available datasets, such as HAM10000 ("Human Against Machine with 10000 training images"), to ensure robust and generalizable results. This evaluation aims to advance the field of melanoma diagnosis by identifying the most effective deep learning approach, thereby facilitating early and accurate detection of this life-threatening disease.