| Autor: |
Thanh DNH; Department of Information Technology, Hue College of Industry, Hue, Vietnam. dnhthanh@hueic.edu.vn., Prasath VBS; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.; Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA.; Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, OH, USA.; Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, USA., Hieu LM; Department of Economics, University of Economics, The University of Danang, Danang, Vietnam., Hien NN; Centre of occupational skills development, Dong Thap University, Cao Lanh, Vietnam. |
| Abstrakt: |
According to statistics of the American Cancer Society, in 2015, there are about 91,270 American adults diagnosed with melanoma of the skin. For the European Union, there are over 90,000 new cases of melanoma annually. Although melanoma only accounts for about 1% of all skin cancers, it causes most of the skin cancer deaths. Melanoma is considered one of the fastest-growing forms of skin cancer, and hence the early detection is crucial, as early detection is helpful and can provide strong recommendations for specific and suitable treatment regimens. In this work, we propose a method to detect melanoma skin cancer with automatic image processing techniques. Our method includes three stages: pre-process images of skin lesions by adaptive principal curvature, segment skin lesions by the colour normalisation and extract features by the ABCD rule. We provide experimental results of the proposed method on the publicly available International Skin Imaging Collaboration (ISIC) skin lesions dataset. The acquired results on melanoma skin cancer detection indicates that the proposed method has high accuracy, and overall, a good performance: for the segmentation stage, the accuracy, Dice, Jaccard scores are 96.6%, 93.9% and 88.7%, respectively; and for the melanoma detection stage, the accuracy is up to 100% for a selected subset of the ISIC dataset. |
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