Title: Automated melanoma skin cancer detection from digital images

Authors: Shalu; Rajneesh Rani; Aman Kamboj

Addresses: Department of Computer Science and Engineering, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, India ' Department of Computer Science and Engineering, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, India ' Department of Computer Science and Engineering, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, India

Abstract: In the early stages, diagnosis of melanoma is important for treating the illness and saving lives. This paper focuses on the development of a system for automatic detection of melanoma skin cancer. The objective of this study is to identify the importance of different colour spaces in melanoma skin cancer detection. Another objective is to compare the colour feature and texture feature to find out that which type of features have more discriminative power to correctly identify melanoma. The whole analysis is done by using the MED-NODE dataset of digital images. This dataset contains a total of 170 images (100 nevi and 70 melanoma). The results show that the combination of features extracted from the hue, saturation and value (HSV) and YCbCr (Y is Luma component and Cb and Cr are two chroma components) colour space give better performance than the features extracted from other colour spaces. Also, the performance of the system is enhanced with the colour features than the performance with texture features. By using features extracted from the HSV and YCbCr colour space, the system shows a more accurate result by giving an accuracy of 84.11% which is higher than the earlier approaches on this dataset.

Keywords: malignant melanoma; skin cancer diagnosis; colour features; texture features; digital image processing; skin cancer detection; medical imaging; the role of machine learning in skin cancer classification.

DOI: 10.1504/IJBET.2021.119928

International Journal of Biomedical Engineering and Technology, 2021 Vol.37 No.3, pp.275 - 289

Received: 17 Aug 2018
Accepted: 31 Oct 2018
Published online: 04 Jan 2022 *

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