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Reseach Article

Papsmear Image based Detection of Cervical Cancer

by Sreedevi M T, Usha B S, Sandya S
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 45 - Number 20
Year of Publication: 2012
Authors: Sreedevi M T, Usha B S, Sandya S
10.5120/7035-9698

Sreedevi M T, Usha B S, Sandya S . Papsmear Image based Detection of Cervical Cancer. International Journal of Computer Applications. 45, 20 ( May 2012), 35-40. DOI=10.5120/7035-9698

@article{ 10.5120/7035-9698,
author = { Sreedevi M T, Usha B S, Sandya S },
title = { Papsmear Image based Detection of Cervical Cancer },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 45 },
number = { 20 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 35-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume45/number20/7035-9698/ },
doi = { 10.5120/7035-9698 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:38:07.766670+05:30
%A Sreedevi M T
%A Usha B S
%A Sandya S
%T Papsmear Image based Detection of Cervical Cancer
%J International Journal of Computer Applications
%@ 0975-8887
%V 45
%N 20
%P 35-40
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, a newapproach is proposed for the early detection of cervical cancer using Papsmear images. Regular Papsmear screening is the most successful attempt of medical science and practice for the early detection of cervical cancer. Manual analysis of the cervical cells is time consuming, laborious and error prone. This paper presents an algorithm for classifying Cervical cells as normal or abnormal. It is tested on 80Papsmear images and the experimental results show that the algorithm is on par with the results obtained by earlier work and gives satisfactory results in terms of sensitivity (100%) and specificity (90%).

References
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Index Terms

Computer Science
Information Sciences

Keywords

Segmentation Feature Extraction Classification Sensitivity Specificity