SCCNet: An Improved Multi-Class Skin Cancer Classification Network using Deep Learning

Skin cancer is the most prevalent type of cancer worldwide, and detecting it early is crucial to a successful course of treatment. In recent years, machine learning methods have demonstrated great potential for making skin cancer detection simpler. Using a model of the fine-tuned Skin Cancer Classification Network (SCCNet) based on deep learning, we proposed a unique method for classifying skin cancer into multiple categories in this research, which is comprised of the Xception deep learning model and additional four types of fine-tuned layers. The proposed model was trained by the publicly available skin cancer dataset ISIC-2018. The dataset consists of seven classes and 21,000 images after data augmentation, with 3,000 images in each class. The proposed model achieved an accuracy, precision, recall, and F1-score of 95.20%, 95.14%, 95.00%, and 95.14% respectively, for seven-class classification. The proposed SCCNet model, achieved an impressive accuracy of 95.20% in the classification of multiple skin cancer classes, outperforms several state-of-the-art approaches, demonstrating its potential to enhance dermatological diagnostics and guide effective therapeutic interventions for patients with skin cancer.