TY  - CONF
ID  - scholars6674
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85005943826&doi=10.1063%2f1.4968145&partnerID=40&md5=a5df4668199561ae3fd3740f91e8951a
N1  - cited By 16; Conference of 4th International Conference on Fundamental and Applied Sciences, ICFAS 2016 ; Conference Date: 15 August 2016 Through 17 August 2016; Conference Code:125141
A1  - Adjed, F.
A1  - Faye, I.
A1  - Ababsa, F.
A1  - Gardezi, S.J.
A1  - Dass, S.C.
N2  - 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).
SN  - 0094243X
TI  - Classification of skin cancer images using local binary pattern and SVM classifier
Y1  - 2016///
VL  - 1787
PB  - American Institute of Physics Inc.
AV  - none
ER  -