Skin cancer classification using machine learning

Abstract

Skin cancer is a major public health problem, especially in the western world, having direct negative impact on life expectancy, quality of life, and on the economy in general. However, early detection of skin cancer could significantly reduce the mortality rate. For this purpose, tremendous efforts have been deployed in recent years for developing machine learning algorithms that can help in the early detection of skin cancer. In this paper, we focus on the classification of the three most common types of skin cancer lesions: Basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma (MEL). Two different convolution neural network (CNN) architectures were implemented for this aim: YOLO, version 7 (v7) using transfer learning and an in-house developed CNN algorithm with optimum number of layers and hyper-parameters. The results obtained by implementing the two algorithms with a total number of 2,792 training samples (after performing data augmentation) show better performance compared to some of the recently published works in the literature. Using YOLO, v7, the average accuracy, sensitivity, and specificity are 89.65%, 85%, and 91.90%, respectively. The aforementioned average values using the proposed CNN algorithm are 90.12%, 85.55%, and 92.57%, respectively.