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Classification of skin cancer images using local binary pattern and SVM classifier

In this paper, a classification method for melanoma and non-melanoma skin cancer images has been presented using the local binary patterns (LBP). The LBP computes the local texture information from the skin cancer images, which is later used to compute some statistical features that have capability...

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Main Authors: Adjed, F., Faye, I., Ababsa, F., Gardezi, S.J., Dass, S.C.
Format: Conference or Workshop Item
Published: American Institute of Physics Inc. 2016
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85005943826&doi=10.1063%2f1.4968145&partnerID=40&md5=a5df4668199561ae3fd3740f91e8951a
http://eprints.utp.edu.my/30677/
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spelling my.utp.eprints.306772022-03-25T07:14:13Z Classification of skin cancer images using local binary pattern and SVM classifier Adjed, F. Faye, I. Ababsa, F. Gardezi, S.J. Dass, S.C. In this paper, a classification method for melanoma and non-melanoma skin cancer images has been presented using the local binary patterns (LBP). The LBP computes the local texture information from the skin cancer images, which is later used to compute some statistical features that have capability to discriminate the melanoma and non-melanoma skin tissues. Support vector machine (SVM) is applied on the feature matrix for classification into two skin image classes (malignant and benign). The method achieves good classification accuracy of 76.1 with sensitivity of 75.6 and specificity of 76.7. © 2016 Author(s). American Institute of Physics Inc. 2016 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85005943826&doi=10.1063%2f1.4968145&partnerID=40&md5=a5df4668199561ae3fd3740f91e8951a Adjed, F. and Faye, I. and Ababsa, F. and Gardezi, S.J. and Dass, S.C. (2016) Classification of skin cancer images using local binary pattern and SVM classifier. In: UNSPECIFIED. http://eprints.utp.edu.my/30677/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description In this paper, a classification method for melanoma and non-melanoma skin cancer images has been presented using the local binary patterns (LBP). The LBP computes the local texture information from the skin cancer images, which is later used to compute some statistical features that have capability to discriminate the melanoma and non-melanoma skin tissues. Support vector machine (SVM) is applied on the feature matrix for classification into two skin image classes (malignant and benign). The method achieves good classification accuracy of 76.1 with sensitivity of 75.6 and specificity of 76.7. © 2016 Author(s).
format Conference or Workshop Item
author Adjed, F.
Faye, I.
Ababsa, F.
Gardezi, S.J.
Dass, S.C.
spellingShingle Adjed, F.
Faye, I.
Ababsa, F.
Gardezi, S.J.
Dass, S.C.
Classification of skin cancer images using local binary pattern and SVM classifier
author_facet Adjed, F.
Faye, I.
Ababsa, F.
Gardezi, S.J.
Dass, S.C.
author_sort Adjed, F.
title Classification of skin cancer images using local binary pattern and SVM classifier
title_short Classification of skin cancer images using local binary pattern and SVM classifier
title_full Classification of skin cancer images using local binary pattern and SVM classifier
title_fullStr Classification of skin cancer images using local binary pattern and SVM classifier
title_full_unstemmed Classification of skin cancer images using local binary pattern and SVM classifier
title_sort classification of skin cancer images using local binary pattern and svm classifier
publisher American Institute of Physics Inc.
publishDate 2016
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85005943826&doi=10.1063%2f1.4968145&partnerID=40&md5=a5df4668199561ae3fd3740f91e8951a
http://eprints.utp.edu.my/30677/
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score 13.211869

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