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Lung Cancer Classification

D.N. Sonar1 , U.V. Kulkarni2

Section:Research Paper, Product Type: Journal Paper
Volume-4 , Issue-12 , Page no. 51-55, Dec-2016

Online published on Jan 02, 2016

Copyright © D.N. Sonar, U.V. Kulkarni . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: D.N. Sonar, U.V. Kulkarni, “Lung Cancer Classification,” International Journal of Computer Sciences and Engineering, Vol.4, Issue.12, pp.51-55, 2016.

MLA Style Citation: D.N. Sonar, U.V. Kulkarni "Lung Cancer Classification." International Journal of Computer Sciences and Engineering 4.12 (2016): 51-55.

APA Style Citation: D.N. Sonar, U.V. Kulkarni, (2016). Lung Cancer Classification. International Journal of Computer Sciences and Engineering, 4(12), 51-55.

BibTex Style Citation:
@article{Sonar_2016,
author = {D.N. Sonar, U.V. Kulkarni},
title = {Lung Cancer Classification},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {12 2016},
volume = {4},
Issue = {12},
month = {12},
year = {2016},
issn = {2347-2693},
pages = {51-55},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1131},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1131
TI - Lung Cancer Classification
T2 - International Journal of Computer Sciences and Engineering
AU - D.N. Sonar, U.V. Kulkarni
PY - 2016
DA - 2017/01/02
PB - IJCSE, Indore, INDIA
SP - 51-55
IS - 12
VL - 4
SN - 2347-2693
ER -

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Abstract

Detection and diagnosis of lung cancer from chest radiographs is one of the most important and difficult task for the radiologists. In this paper, combination of statistical texture and moment invariant features are used to classify the lung cancer images. These features are extracted from JSRT raw chest X-ray images. The proposed approach is built on two-level architecture. In the first level architecture images are sharpened and segmented to extract the region of interest i.e. lung from the ribs using image processing techniques. In second level architecture, statistical texture and moment invariant based features are extracted depending on the shape characteristics of the region. These features are used as input pattern to the Fuzzy Hypersphere Neural Network (FHSNN) classifier. The experimental result shows that proposed approach is superior in comparison with only statistical texture features in terms of recognition rate, training and testing time.

Key-Words / Index Term

Chest Radiography, Computer Tomography (CT), Fuzzy Hypersphere Neural Network (FHSNN), Lung Nodule, Gray level co-occurrence matrix (GLCM)

References

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