Author(s): Atul Kulkarni, Debajyoti Mukhopadhyay

Email(s): Email ID Not Available

DOI: 10.5958/0974-360X.2017.00808.3   

Address: Atul Kulkarni1, Dr. Debajyoti Mukhopadhyay2
1Research Scholar, Information Technology, AMET University, Chennai.
2Department of Computer Science, Maharashtra Institute of Technology, Chennai.
*Corresponding Author

Published In:   Volume - 10,      Issue - 12,     Year - 2017


ABSTRACT:
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.


Cite this article:
Atul Kulkarni, Debajyoti Mukhopadhyay. SVM Classifier Based Melanoma Image Classification. Research J. Pharm. and Tech 2017; 10(12): 4391-4392. doi: 10.5958/0974-360X.2017.00808.3

Cite(Electronic):
Atul Kulkarni, Debajyoti Mukhopadhyay. SVM Classifier Based Melanoma Image Classification. Research J. Pharm. and Tech 2017; 10(12): 4391-4392. doi: 10.5958/0974-360X.2017.00808.3   Available on: https://www.rjptonline.org/AbstractView.aspx?PID=2017-10-12-59


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RNI: CHHENG00387/33/1/2008-TC                     
DOI: 10.5958/0974-360X 

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