Skin Cancer Classification Using Transfer Learning-Based Pre-Trained VGG 16 Model

Skin is one of the most dangerous types of cancer which affect, millions of people every year. The identification and treatment of skin cancer is challenging and expensive due to the requirements of advanced technologies. According to recent studies, dermatologists can categorise medical images with the aid of machine learning and deep learning-based methods. Skin cancer is currently one of the most prevalent and deadliest forms of cancer if it reaches to the malignant stage. Melanoma is a skin cancer which occurs when the skin undergoes through various abnormal changes. To reduce the effort, time, and risk involved with skin cancer, an accurate automated system for skin lesion classification is required. Due to the complexity of the skin's texture and the visual proximity of the disease, it becomes very difficult to accurately identify this type of cancer. In this work, a pre-trained VGG16 model has been used for the prediction of skin cancer. The results of the proposed model have been identified in terms of AUC and loss at the validation and training phases. These results have been identified by performing fine-tuning of the pre-trained VGG16 model and keeping the epoch values as 2, 10, and 20. The performance of the model has been analyzed which identified that the best results of AUC have been identified at epoch 20 with the ROC value of 0.841 and loss value of 0.034. In future, this work can be expanded with the implementation of transfer learning models to achieve the performance in terms of accuracy.