Melanoma Classification Using Deep Learning

The prevalence of skin cancer, specifically melanoma, constitutes a significant global health concern, thus giving rise to intricate detection challenges that demand immediate attention and comprehensive solutions. In this study, we investigate the application of deep learning models for melanoma detection. Five pre-trained models, including VGG-16, ResNet50, InceptionV3, DenseNet-121, and Xception, are evaluated through a series of experiments. The models undergo the same training process with transfer learning, freezing all layers and modifying the classification layer. The experiments reveal that ResNet50 consistently outperforms the other models, demonstrating superior accuracy, precision, recall, and F1 score. Notably, ResNet50 exhibits exceptional accuracy and F1 score, achieving around 93% in both. This study sheds light on the potential use of deep learning in enhancing melanoma diagnosis and underscores the need for robust and accurate classification systems for early detection and effective treatment of skin cancer.