An automated skin melanoma detection system with melanoma-index based on entropy features
Article
Cheong, Kang Hao, Tang, Kenneth Jian Wei, Zhao, Xinxing, Koh, Joel En Wei, Faust, Oliver ., Gururajan, Raj, Ciaccio, Edward J., Rajinikanth, V. and Acharya, U. Rajendra. 2021. "An automated skin melanoma detection system with melanoma-index based on entropy features." Biocybernetics and Biomedical Engineering. 41 (3), pp. 997-1012. https://doi.org/10.1016/j.bbe.2021.05.010
| Article Title | An automated skin melanoma detection system with melanoma-index based on entropy features |
|---|---|
| ERA Journal ID | 211872 |
| Article Category | Article |
| Authors | Cheong, Kang Hao, Tang, Kenneth Jian Wei, Zhao, Xinxing, Koh, Joel En Wei, Faust, Oliver ., Gururajan, Raj, Ciaccio, Edward J., Rajinikanth, V. and Acharya, U. Rajendra |
| Journal Title | Biocybernetics and Biomedical Engineering |
| Journal Citation | 41 (3), pp. 997-1012 |
| Number of Pages | 16 |
| Year | 2021 |
| Publisher | Elsevier BV |
| Place of Publication | Netherlands |
| ISSN | 0208-5216 |
| 2391-467X | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.bbe.2021.05.010 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0208521621000644 |
| Abstract | Skin melanoma is a potentially life-threatening cancer. Once it has metastasized, it may cause severe disability and death. Therefore, early diagnosis is important to improve the conditions and outcomes for patients. The disease can be diagnosed based on Digital-Dermoscopy (DD) images. In this study, we propose an original and novel Automated Skin-Melanoma Detection (ASMD) system with Melanoma-Index (MI). The system incorporates image pre-processing, Bi-dimensional Empirical Mode Decomposition (BEMD), image texture enhancement, entropy and energy feature mining, as well as binary classification. The system design has been guided by feature ranking, with Student’s t-test and other statistical methods used for quality assessment. The proposed ASMD was employed to examine 600 benign and 600 DD malignant images from benchmark databases. Our classification performance assessment indicates that the combination of Support Vector Machine (SVM) and Radial Basis Function (RBF) offers a classification accuracy of greater than 97.50%. Motivated by these classification results, we also formulated a clinically relevant MI using the dominant entropy features. Our proposed index can assist dermatologists to track multiple information-bearing features, thereby increasing the confidence with which a diagnosis is given. |
| Keywords | Bi-dimensional empirical mode decomposition; Skin melanoma; Feature extraction; Classification; Melanoma-index formulation |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Singapore University of Technology and Design |
| Ngee Ann Polytechnic, Singapore | |
| Sheffield Hallam University, United Kingdom | |
| School of Business | |
| Columbia University Irving Medical Center, United States | |
| St. Joseph's College of Engineering, India | |
| Singapore University of Social Sciences (SUSS), Singapore | |
| Kumamoto University, Japan | |
| Asia University, Taiwan |
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