A CNN Based Approach to Classify Skin Cancers using Transfer Learning

Skin cancer is one of the most frequent types of cancer in the world. Malignant skin cancers can become life-threatening if not treated in an early stage. Some skin cancers like Squamous Cell Carcinoma (SCC), Melanoma (MEL), and Basal Cell Carcinoma (BCC) are always malignant and can cause fatal damage to the skin. Therefore, early identification is essential for minimizing the harm. This study uses transfer learning and explores the famous pre-trained model, Xception, with necessary fine tuning to classify eight types of skin cancers. During the training of the proposed model, data augmentation techniques are used to introduce diversity in the training phase. The proposed method is tested on the International Skin Imaging Collaboration (ISIC) 2019 dataset. It exhibits better results than similar studies which can help early detection of malignant skin cancers.