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

Faouzi Adjed 1, 2, 3 Ibrahima Faye 2, 3 Fakhr-Eddine Ababsa 1 Syed Jamal Gardezi 2, 3 Sarat Chandra Dass 2, 3
1 IBISC - Informatique, Biologie Intégrative et Systèmes Complexes
2 CISIR - Centre for Intelligent Signal and Imaging Research (Universiti Teknologi Petronas)
3 Department of Fundamental & Applied Sciences
UTP - university technologic Petronas
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%.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01423330
Contributor : Frédéric Davesne <>
Submitted on : Thursday, December 29, 2016 - 2:25:22 PM
Last modification on : Monday, October 28, 2019 - 10:50:22 AM

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IBISC | UNIV-EVRY | IBISC-IRA2 | UNIV-PARIS-SACLAY

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Faouzi Adjed, Ibrahima Faye, Fakhr-Eddine Ababsa, Syed Jamal Gardezi, Sarat Chandra Dass. Classification of skin cancer images using local binary pattern and SVM classifier. 4th International Conference on Fundamental and Applied Sciences (ICFAS 2016), Aug 2016, Kuala Lumpur, Malaysia. (elec. proc.), ⟨10.1063/1.4968145⟩. ⟨hal-01423330⟩

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