Volume 5, Issue 5, September 2017, Page: 58-62
Detection of Lung Cancer Using Digital Image Processing Techniques: A Comparative Study
Munimanda Prem Chander, Department of Computer Science and Engineering GIT, GITAM UNIVERSITY, Visakhapatnam, India
M. Venkateshwara Rao, Department of Information Technology, GIT, GITAM UNIVERSITY, Visakhapatnam, India
T. V. Rajinikanth, Department of Computer Science and Engineering, Srinidhi Institute of Science and Technology, Hyderabad, India
Received: Apr. 16, 2017;       Accepted: May 8, 2017;       Published: Dec. 9, 2017
DOI: 10.11648/j.ijmi.20170505.12      View  2058      Downloads  126
Abstract
This paper focuses on early stage lung cancer detection. Lung cancer is prominent cancer as it states large number of deaths of more than a million every year. It creates need of detecting the lung nodule at early stage in Computer Tomography medical images. So to detect the occurrence of cancer nodule at early stage, the requirement of methods and techniques is increasing. There are different methods and techniques existing but none of them provide a better accuracy of detection. One of the techniques is content based image retrieval Computer Aided Diagnosis System (CAD) for early detection of lung nodules from the Chest Computer Tomography (CT) images. This optimization algorithm allows physicians to identify the nodules present in the CT lung images in the early stage hence the lung cancer. The MATLAB image processing toolbox based implementation is done on the CT lung images and the classifications of these images are carried out. The performance measures like the classification rate and the false positive rates are analyzed.
Keywords
Classification, Lung Cancer Detection, Accuracy, Image Processing Techniques
To cite this article
Munimanda Prem Chander, M. Venkateshwara Rao, T. V. Rajinikanth, Detection of Lung Cancer Using Digital Image Processing Techniques: A Comparative Study, International Journal of Medical Imaging. Vol. 5, No. 5, 2017, pp. 58-62. doi: 10.11648/j.ijmi.20170505.12
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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