DocumentCode :
1776339
Title :
Automatic detection of lung nodules using classifiers
Author :
Thomas, Renu Ann ; Kumar, Sahoo Subhendu
Author_Institution :
Control & Instrum., Noorul Islam Univ., Thuckalay, India
fYear :
2014
fDate :
10-11 July 2014
Firstpage :
705
Lastpage :
710
Abstract :
In this paper, comparison between three classifiers for lung cancer diagnosis is proposed. Morphological Operations is used for preprocessing of the images and gray level cooccurrence matrix is used for the feature extraction process and SVM, Minimum distance and k-nearest neighbor classifiers are used for classification. Experimental analysis is made with data set to evaluate the performance of the different classifiers. The performance of SVM classifiers is found to be the best based correct and incorrect classification of the classifier.
Keywords :
cancer; feature extraction; image classification; image segmentation; lung; matrix algebra; medical image processing; object detection; support vector machines; SVM classifiers; automatic lung nodule detection; feature extraction process; gray level cooccurrence matrix; image preprocessing; k-nearest neighbor classifiers; lung cancer diagnosis; minimum distance classifiers; morphological operations; Accuracy; Cancer; Feature extraction; Image segmentation; Instruments; Lungs; Support vector machines; Classifiers; Preprocssing; Segmentation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2014 International Conference on
Conference_Location :
Kanyakumari
Print_ISBN :
978-1-4799-4191-9
Type :
conf
DOI :
10.1109/ICCICCT.2014.6993051
Filename :
6993051
Link To Document :
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