A fuzzy shape representation of a segmented vessel tree and kernel-induced random forest classifier for the efficient prediction of lung cancer
An intelligent clinical decision support system is proposed classifying lung nodules for lung cancer prediction using a kernel-induced random forest classifier. A contourlet filter is used for image denoising. Fuzzy logic is used to represent the segmented shape of a vessel tree. The fuzzy shape of...
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| Published in | The Journal of supercomputing Vol. 76; no. 8; pp. 5801 - 5824 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.08.2020
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0920-8542 1573-0484 |
| DOI | 10.1007/s11227-019-03002-5 |
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| Abstract | An intelligent clinical decision support system is proposed classifying lung nodules for lung cancer prediction using a kernel-induced random forest classifier. A contourlet filter is used for image denoising. Fuzzy logic is used to represent the segmented shape of a vessel tree. The fuzzy shape of the vessel tree is then given to a classifier as a feature for learning. A hybridization of expected maximization and total variation regularisation is proposed for the vessel tree segmentation. The proposed use of a fuzzy shape vessel tree and kernel-induced random forest classifier promises to be an efficient method of detecting lung nodules for cancer diagnosis. The proposed system is evaluated for precision, recall and accuracy in comparison with many previously available techniques. |
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| AbstractList | An intelligent clinical decision support system is proposed classifying lung nodules for lung cancer prediction using a kernel-induced random forest classifier. A contourlet filter is used for image denoising. Fuzzy logic is used to represent the segmented shape of a vessel tree. The fuzzy shape of the vessel tree is then given to a classifier as a feature for learning. A hybridization of expected maximization and total variation regularisation is proposed for the vessel tree segmentation. The proposed use of a fuzzy shape vessel tree and kernel-induced random forest classifier promises to be an efficient method of detecting lung nodules for cancer diagnosis. The proposed system is evaluated for precision, recall and accuracy in comparison with many previously available techniques. |
| Author | Deepa, P. Suganthi, M. |
| Author_xml | – sequence: 1 givenname: P. surname: Deepa fullname: Deepa, P. email: Deeparam2000@gmail.com, p_deepacse@yahoo.com organization: Department of Computer Science and Engineering, Muthayammal Engineering College – sequence: 2 givenname: M. surname: Suganthi fullname: Suganthi, M. organization: Department of Electronics and Communication Engineering, Mahendra College of Engineering |
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| Cites_doi | 10.1016/j.neucom.2015.05.044 10.1109/TITB.2011.2112668 10.1016/j.media.2010.02.004 10.1016/j.eswa.2012.03.008 10.1109/ACCESS.2014.2373335 10.1038/s41598-017-14513-7 10.1016/j.ins.2012.05.008 10.1155/2015/230830 10.7763/IJBBB.2013.V3.289 10.1016/j.compbiomed.2012.09.003 10.1016/j.jvcir.2012 10.1016/j.patcog.2012.10.005 10.1016/j.cmpb.2009.07.006 10.1016/j.cmpb.2013.08.015 10.1109/TBME.2011.2167621 10.9790/0661-16180511 10.3844/jcssp.2015.195.203 10.1016/j.compbiomed.2012.12.004 10.5566/ias.v33.p13-27 |
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| Keywords | Contourlet filter Kernel-induced random forest technique Total variation regularisation Fuzzy shape representation Lung nodules EM algorithm |
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| SubjectTerms | Classifiers Compilers Computer Science Decision support systems Decision trees Fuzzy logic Image filters Image segmentation Interpreters Kernels Lung cancer Nodules Noise reduction Processor Architectures Programming Languages Regularization Vessels |
| Title | A fuzzy shape representation of a segmented vessel tree and kernel-induced random forest classifier for the efficient prediction of lung cancer |
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