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Reseach Article

Comparison between Different Classification Methods with Application to Skin Cancer

by Yogendra Kumar Jain, Megha Jain
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 53 - Number 11
Year of Publication: 2012
Authors: Yogendra Kumar Jain, Megha Jain
10.5120/8465-2386

Yogendra Kumar Jain, Megha Jain . Comparison between Different Classification Methods with Application to Skin Cancer. International Journal of Computer Applications. 53, 11 ( September 2012), 18-24. DOI=10.5120/8465-2386

@article{ 10.5120/8465-2386,
author = { Yogendra Kumar Jain, Megha Jain },
title = { Comparison between Different Classification Methods with Application to Skin Cancer },
journal = { International Journal of Computer Applications },
issue_date = { September 2012 },
volume = { 53 },
number = { 11 },
month = { September },
year = { 2012 },
issn = { 0975-8887 },
pages = { 18-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume53/number11/8465-2386/ },
doi = { 10.5120/8465-2386 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:53:51.699010+05:30
%A Yogendra Kumar Jain
%A Megha Jain
%T Comparison between Different Classification Methods with Application to Skin Cancer
%J International Journal of Computer Applications
%@ 0975-8887
%V 53
%N 11
%P 18-24
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In recent years, skin cancer is the most common form of human cancer. It is estimated that over 1 million new cases occur annually. In order to detect skin cancer various methods have been proposed in the past decades. This paper focuses on the development of a skin cancer screening system that can be used in a general practice by non-experts to classify normal from abnormal cases. The development process consists of Feature Detection and Classification Technique. The features are extracted by decomposing images into different frequency sub-bands using wavelet transform. The output of Discrete Wavelet Transform becomes input to the Classification System which classify whether the input image is cancerous or noncancerous. The classification system is based on the application of Probabilistic Neural Network and Clustering Classifier. The Accuracy of the proposed system is calculated using different classification techniques on image database of 80 samples (40 cancerous and 40 non cancerous images).

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Index Terms

Computer Science
Information Sciences

Keywords

Object Detection Contour Tracing Algorithm Feature Extraction Discrete Wavelet Transform Probabilistic Neural Network Clustering Classifier