Skin Cancer Data Augementation Method Based on Color Transfer
Conference: CAIBDA 2022 - 2nd International Conference on Artificial Intelligence, Big Data and Algorithms
06/17/2022 - 06/19/2022 at Nanjing, China
Proceedings: CAIBDA 2022
Pages: 5Language: englishTyp: PDF
Authors:
Chen, Zhe (ANU College of Engineering and Computer science, Australian National University, Canberra, Australia)
Guo, Xiaoyan (School of Communication Engineering, Xidian University, Xi’an, China)
Liu, Jianheng (School of Automation Science and Electrical Engineering, Beihang University, Beijing, China)
Yu, Pengyuan (School of Software Engineering, South China University of Technology, Guangzhou, China)
Abstract:
There are eight common types of skin cancer. Using deep learning is a popular way of helping users to classify whether their lesion is benign or malignant. If they could observe their disease as soon as possible, it can improve patients’ survival chances. However, the datasets for the medical field have joint problems. That is, the size of the datasets is rare. The popular skin cancer dataset that we use frequently has few apparent issues. The amount of the different types is gigantic, and the total amount of the images is not rare. Also, most of the image data are collected from white skin tone patients. Our work put forward a new model that could improve these situations. The most significant contribution of our work is that it let the data not only focus on one type of color skin tone but is able to generate around 42 different skin tones.