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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American Institute of Physics Inc.
2016
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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/ |
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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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1738657141041922048 |
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13.251813 |