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Reseach Article

Comparative Study on Cancer Image Diagnosis using Soft Computing Techniques

by V.Sivakrithika, B.Shanthi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 19 - Number 5
Year of Publication: 2011
Authors: V.Sivakrithika, B.Shanthi
10.5120/2358-3087

V.Sivakrithika, B.Shanthi . Comparative Study on Cancer Image Diagnosis using Soft Computing Techniques. International Journal of Computer Applications. 19, 5 ( April 2011), 19-23. DOI=10.5120/2358-3087

@article{ 10.5120/2358-3087,
author = { V.Sivakrithika, B.Shanthi },
title = { Comparative Study on Cancer Image Diagnosis using Soft Computing Techniques },
journal = { International Journal of Computer Applications },
issue_date = { April 2011 },
volume = { 19 },
number = { 5 },
month = { April },
year = { 2011 },
issn = { 0975-8887 },
pages = { 19-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume19/number5/2358-3087/ },
doi = { 10.5120/2358-3087 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:06:12.044836+05:30
%A V.Sivakrithika
%A B.Shanthi
%T Comparative Study on Cancer Image Diagnosis using Soft Computing Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 19
%N 5
%P 19-23
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Computer-aided diagnosis system (CAD) can be very helpful for radiologist in detection and diagnosing abnormalities earlier and faster than traditional screening programs. CAD as such employs several techniques to accomplish this task. In this paper, we propose to make a comparative study of two classification methods: One in which we utilize the texture features extracted from the images by directly feeding to the Neural Network based classifier stage to classify the images into benign or malign and in the other hybrid method, those texture features are made to undergo fuzzy discretization before feeding to the Neural Network classifier for the classification. The studies so far conducted using both the systems show that the hybrid system is far superior to the first method in its accuracy. Backward Propagation Network (BPN) algorithm is used in the training stage.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Feature Extraction Fuzzy Neural Network