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Research Journal of Pharmacy and Technology
Year : 2017, Volume : 10, Issue : 12
First page : ( 4391) Last page : ( 4392)
Print ISSN : 0974-3618. Online ISSN : 0974-360X.
Article DOI : 10.5958/0974-360X.2017.00808.3

SVM Classifier Based Melanoma Image Classification

Kulkarni Atul1, Dr. Mukhopadhyay Debajyoti2

1Research Scholar, Information Technology, AMET University, Chennai

2Department of Computer Science, Maharashtra Institute of Technology, Chennai

Corresponding Author E-mail:

Online published on 26 March, 2018.


Melanoma Classification is the most important aspect that is related to the patients who endures melanoma. The melanoma is usually known by measuring the depth given in millimeters (mm) and is evaluated by the pathological assessment. In order to avoid the interference method usage in the surgery, a method is proposed for computational image analysis. In the system the Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) algorithms are used for the features extraction process and those features are classified by using the Support Vector Machine (SVM) classifier. The proposed melanoma classification gives the output accuracy of about 96.7% of classification accuracy value.



Melanoma, dermoscopic, GLCM, LBP, etc.


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