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