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Computers 2016, 5(3), 13; doi:10.3390/computers5030013

Prediction of Dermoscopy Patterns for Recognition of both Melanocytic and Non-Melanocytic Skin Lesions

1
College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
2
Department of Computer Science, Comsats Institute of Information Technology, Sahiwal 57000, Pakistan
*
Author to whom correspondence should be addressed.
Academic Editor: Yevgeniya Kovalchuk
Received: 23 April 2016 / Revised: 17 June 2016 / Accepted: 20 June 2016 / Published: 24 June 2016
View Full-Text   |   Download PDF [4371 KB, uploaded 27 June 2016]   |  

Abstract

A differentiation between all types of melanocytic and non-melanocytic skin lesions (MnM–SK) is a challenging task for both computer-aided diagnosis (CAD) and dermatologists due to the complex structure of patterns. The dermatologists are widely using pattern analysis as a first step with clinical attributes to recognize all categories of pigmented skin lesions (PSLs). To increase the diagnostic accuracy of CAD systems, a new pattern classification algorithm is proposed to predict skin lesions patterns by integrating the majority voting (MV–SVM) scheme with multi-class support vector machine (SVM). The optimal color and texture features are also extracted from each region-of-interest (ROI) dermoscopy image and then these normalized features are fed into an MV–SVM classifier to recognize seven classes. The overall system is evaluated using a dataset of 350 dermoscopy images (50 ROIs per class). On average, the sensitivity of 94%, specificity of 84%, 93% of accuracy and area under the receiver operating curve (AUC) of 0.94 are achieved by the proposed MnM–SK system compared to state-of-the-art methods. The obtained result indicates that the MnM–SK system is successful for obtaining the high level of diagnostic accuracy. Thus, it can be used as an alternative pattern classification system to differentiate among all types of pigmented skin lesions (PSLs). View Full-Text
Keywords: skin cancer; pattern recognition; computer-aided detection; color and texture features; support vector machine; majority voting scheme skin cancer; pattern recognition; computer-aided detection; color and texture features; support vector machine; majority voting scheme
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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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MDPI and ACS Style

Abbas, Q.; Sadaf, M.; Akram, A. Prediction of Dermoscopy Patterns for Recognition of both Melanocytic and Non-Melanocytic Skin Lesions. Computers 2016, 5, 13.

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