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dc.contributor.authorEltayef, K-
dc.contributor.authorLi, Y-
dc.contributor.authorDodo, BI-
dc.contributor.authorLiu, X-
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, 10584 LNCS pp. 75 - 86en_US
dc.description.abstractAbstract. In the medical field, the identification of skin cancer (Malignant Melanoma) in dermoscopy images is still a challenging task for radiologists and researchers. Due to its rapid increase, the need for decision support systems to assist the radiologists to detect it in early stages becomes essential and necessary. Computer Aided Diagnosis (CAD) systems have significant potential to increase the accuracy of its early detection. Typically, CAD systems use various types of features to characterize skin lesions. The features are often concatenated into one vector (early fusion) to represent the image. In this paper, we present a novel method for melanoma detection from images. First the lesions are segmented by combining Particle Swarm Optimization and Markov Random Field methods. Then the K-means is applied on the segmented lesions to separate them into homogeneous clusters, from which important features are extracted. Finally, an Artificial Neural Network with Radial Basis Function is applied for the detection of melanoma. The method was tested on 200 dermoscopy images. The experimental results show that the proposed method achieved higher accuracy in terms of melanoma detection, compared to alternative methods.en_US
dc.format.extent75 - 86-
dc.sourceIDA 2017-
dc.sourceIDA 2017-
dc.subjectMelanoma detectionen_US
dc.subject· Skin cancer ·en_US
dc.subject· Dermoscopy imagesen_US
dc.subjectSkin lesionen_US
dc.titleSkin Cancer Detection in Dermoscopy Images Using Sub-Region Featuresen_US
dc.typeConference Paperen_US
dc.relation.isPartOfLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
pubs.volume10584 LNCS-
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