Open AccessClassification of Dermoscopic Image of Skin Cancer Using the GLCM Method and Multi-SVM Algorithm
Author(s) -
Riyan Latifahul Hasanah,
Dwiza Riana
Publication year - 2021
Publication title -
rekayasa
Language(s) - English
Resource type - Journals
eISSN - 2502-5325
pISSN - 0216-9495
DOI - 10.21107/rekayasa.v14i3.12213
Subject(s) - artificial intelligence , support vector machine , skin cancer , melanoma , rgb color model , pattern recognition (psychology) , grayscale , computer science , segmentation , image processing , feature extraction , dermatology , computer vision , cancer , medicine , image (mathematics) , cancer research
The development of abnormal skin pigment cells can cause a skin cancer called melanoma. Melanoma can be cured if diagnosed and treated in its early stages. Various studies using various technologies have been developed to conduct early detection of melanoma. This research was conducted to diagnose melanoma skin cancer with digital image processing techniques on the dermoscopic image of skin cancer. The diagnosis is made by classifying dermoscopic images based on the types of Common Nevus, Atypical Nevus or Melanoma. Pre-processing is done by changing the RGB image to grayscale (grayscaling), smoothing image using median filtering, and image segmentation based on binary images of skin lesions. The value of Contrast, Correlation, Energy and Homogeneity obtained from the texture feature extraction of the GLCM method is used in the next step, which is the classification process with the Multi-SVM algorithm. The proposed research method shows high accuracy results in diagnosing skin cancer