International Journal of Emerging Trends & Technology in Computer Science
A Motivation for Recent Innovation & Research
ISSN 2278-6856
www.ijettcs.org
Call for Paper, Published Articles, Indexing Infromation
Title: |
Machine learning Technique for detection of
Cervical Cancer using k-NN and Artificial
Neural Network
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Author Name: |
Priyanka K Malli , Dr. Suvarna Nandyal |
Abstract: |
Abstract
Cervical cancer along with micro classification are one of the 2
major forms of cancer being observed amongst women across
the globe. A cervical cancer results in dead nucleus or change
in the morphology of the cells in the cervix. Such cells may have
multiple nucleuses ,faulty cytoplasm ,lack of cytoplasm
,dissolved lack of cytoplasm and so on. Detection of cervical
cancer in a microscopic smear Test (fluid taken from the cervix
)smear is analyzed to microscope is extremely challenging
because such cells does not offer significant color or texture
variations from the normal cells .Therefore high level Digital
Image Processing technique are required identify
abnormalities in human cell related cancer detection system.
Therefore an automated, comprehensive machine learning
technique has been proposed in this work. The proposed
technique gives that color and shape features of nucleus and
cytoplasm of the cervix cell. The nucleus and the cytoplasm are
separated from the cell use the advanced fuzzy based technique.
KNN and Neural network are trained with the shape features
and color features of the segmented units of the cell and then
an unknown cervix cell samples are classified by this technique.
The classification result have shown an accuracy of 88.04% for
KNN and 54% for ANN. The proposed system work can be
further enhanced by taking other classifiers.
Keywords: Pap smear Images, Feature Extraction, KNN,
ANN |
Cite this article: |
Priyanka K Malli , Dr. Suvarna Nandyal , "
Machine learning Technique for detection of
Cervical Cancer using k-NN and Artificial
Neural Network " , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS),
Volume 6, Issue 4, July - August 2017 , pp.
145-149 , ISSN 2278-6856.
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