An automated skin melanoma detection system with melanoma-index based on entropy features

• Autorzy: Cheong Kang Hao , Tang Kenneth Jian Wei , Zhao Xinxing , Koh Joel En Wei , Faust Oliver , Gururajan Raj , Ciaccio Edward J. , Rajinikanth V. , Acharya U. Rajendra

Identyfikatory

DOI

10.1016/j.bbe.2021.05.010

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

skin melanoma; digital dermoscopy; Bi-dimensional empirical mode decomposition; feature extraction; melanoma index formulation

PL

czerniak skóry; dermoskopia cyfrowa; empiryczna dekompozycja sygnału; ekstrakcja cech

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2021
• Tom: Vol. 41, no. 3
• Strony: 997--1012
• Opis fizyczny: Bibliogr. 84 poz., rys., tab., wykr.

Twórcy

autor

Cheong Kang Hao

• Science, Mathematics and Technology Cluster, Singapore University of Technology and Design S487372, Singapore

autor

Tang Kenneth Jian Wei

• Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, Singapore

autor

Zhao Xinxing

• Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, Singapore

autor

Koh Joel En Wei

• Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore

autor

Faust Oliver

• Department of Engineering and Mathematics, Sheffield Hallam University, United Kingdom

autor

Gururajan Raj

• School of Business, University of Southern Queensland, Springfield, Australia

autor

Ciaccio Edward J.

• Department of Medicine, Columbia University Medical Center, New York, USA

autor

Rajinikanth V.

• Department of Electronics and Instrumentation, St. Joseph’s College of Engineering, Chennai, India

autor

Acharya U. Rajendra

• Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; International Research Organization for Advanced Science and Technology (IROAST) Kumamoto University, Kumamoto, Japan; Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).