Classification of Skin Cancer Images Using Local Binary Pattern and SVM Classifier

Faouzi Adjed, Ibrahima Faye, Fakhreddine Ababsa, Syed Jamal Gardezi, Sarat Chandra Dass

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

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%.
Original languageEnglish
Title of host publicationProceeding of the 4th International Conference of Fundamental and Applied Sciences 2016
PublisherAIP Publishing
ISBN (Print)9780735414518
DOIs
Publication statusPublished - 1 Nov 2016

Publication series

NameAIP Conference Proceedings
Number1
Volume1787
ISSN (Print)0094-243X

Cite this

Adjed, F., Faye, I., Ababsa, F., Gardezi, S. J., & Dass, S. C. (2016). Classification of Skin Cancer Images Using Local Binary Pattern and SVM Classifier. In Proceeding of the 4th International Conference of Fundamental and Applied Sciences 2016 [080006] (AIP Conference Proceedings; Vol. 1787, No. 1). AIP Publishing. https://doi.org/10.1063/1.4968145