Manuscript received June 9, 2023; revised July 4, 2023; accepted July 26, 2023.

Abstract—Skin cancer has become the fifth-most dangerous type of cancer. Melanoma, the most ferocious type of skin cancer, should be detected and treated to reduce the risk of spreading to the rest of the body’s organs. This study aims to provide fast and painless detection of skin cancer using image processing, including enhancement and extraction of interesting features for the characterization and classification of infected skin images into melanoma or nonmelanoma in MATLAB. The features used for texture analysis of inserted images are the Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP). The classification of melanoma and non-melanoma is done by training a Support Vector Machine (SVM) using the radial basis function kernel. The accuracy of testing is 94.87%.

Keywords—Melanoma, Support Vector Machine (SVM), Radial Bases Function (RBF), image processing, Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), MATLAB

Cite: Radhwan M. W. Khaleel and Nasseer M. Basheer, "Melanoma Detection Based on SVM Using MATLAB," Journal of Image and Graphics, Vol. 11, No. 4, pp. 353-358, December 2023.

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