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Computation 2017, 5(1), 4; doi:10.3390/computation5010004

An SVM Framework for Malignant Melanoma Detection Based on Optimized HOG Features

Department of Mathematics and Computer Science, Faculty of Science, Sohag University, 82524 Sohag, Egypt
Institute for Information Technology and Communications, Otto-von-Guericke-University Magdeburg, P.O. Box 4120, 39016 Magdeburg, Germany
Academic Editor: Rainer Breitling
Received: 19 September 2016 / Revised: 8 December 2016 / Accepted: 15 December 2016 / Published: 1 January 2017
(This article belongs to the Section Computational Biology)
View Full-Text   |   Download PDF [1228 KB, uploaded 1 January 2017]   |  


Early detection of skin cancer through improved techniques and innovative technologies has the greatest potential for significantly reducing both morbidity and mortality associated with this disease. In this paper, an effective framework of a CAD (Computer-Aided Diagnosis) system for melanoma skin cancer is developed mainly by application of an SVM (Support Vector Machine) model on an optimized set of HOG (Histogram of Oriented Gradient) based descriptors of skin lesions. Experimental results obtained by applying the presented methodology on a large, publicly accessible dataset of dermoscopy images demonstrate that the proposed framework is a strong contender for the state-of-the-art alternatives by achieving high levels of sensitivity, specificity, and accuracy (98.21%, 96.43% and 97.32%, respectively), without sacrificing computational soundness. View Full-Text
Keywords: melanoma skin cancer; CAD; dermoscopy; HOG descriptors; SVM classification melanoma skin cancer; CAD; dermoscopy; HOG descriptors; SVM classification

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Bakheet, S. An SVM Framework for Malignant Melanoma Detection Based on Optimized HOG Features. Computation 2017, 5, 4.

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