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Melanoma Classification Algorithm using Gray-level Conversion Matrix Feature and Support Vector Machine

회색도 변환 행렬 특징과 SVM을 이용한 흑색종 분류 알고리즘

  • Koo, Jung Mo (Dept. of Medical & Biological Eng., Graduate School, Kyungpook National University) ;
  • Na, Sung Dae (Dept. of Medical & Biological Eng., Graduate School, Kyungpook National University) ;
  • Cho, Jin-Ho (School of Electronics Engineering, College of IT Engineering, Kyungpook National University) ;
  • Kim, Myoung Nam (Dept. of Biomedical Eng., School of Medicine, Kyungpook National University)
  • Received : 2018.01.11
  • Accepted : 2018.01.23
  • Published : 2018.02.28
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Abstract

Recently, human life is getting longer due to change of living environment and development of medical technology, and silver medical technology has been in the limelight. Geriatric skin disease is difficult to detect early, and when it is missed, it becomes a malignant disease and is difficult to treatment. Melanoma is one of the most common diseases of geriatric skin disease and initially has a similar modality with the nevus. In order to overcome this problem, we attempted to perform a feature analysis in order to attempt automatic detection of melanoma-like lesions. In this paper, one is first order analysis using information of pixels in radiomic feature. The other is a gray-level co-occurrence matrix and a gray level run length matrix, which are feature extraction methods for converting image information into a matrix. The features were extracted through these analyses. And classification is implemented by SVM.

Keywords

References

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