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A Hybrid Automatic System for the Diagnosis of Lung Cancer Based on Genetic Algorithm and Fuzzy Extreme Learning Machines

Author:

Mohammad Reza DaliriAuthors Info & Claims

Journal of Medical Systems, Volume 36, Issue 2

Pages 1001 - 1005

Published: 01 April 2012 Publication History

Abstract

An automatic system for the diagnosis of lung cancer has been proposed in this manuscript. The proposed method is based on combination of genetic algorithm (GA) for the feature selection and newly proposed approach, namely the extreme learning machines (ELM) for the classification of lung cancer data. The dimension of the feature space is reduced by the GA in this scheme and the effective features are selected in this way. The data are then fed to a fuzzy inference system (FIS) which is trained by the fuzzy extreme learning machines approach. The results on real data indicate that the proposed system is very effective in the diagnosis of lung cancer and can be used for clinical applications.

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Cited By

Kiani RJin WSheng V(2024)Survey on extreme learning machines for outlier detectionMachine Language10.1007/s10994-023-06375-0113:8(5495-5531)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s10994-023-06375-0
Davoudi SAhmadi ADaliri M(2021)Frequency–amplitude coupling: a new approach for decoding of attended features in covert visual attention taskNeural Computing and Applications10.1007/s00521-020-05222-w33:8(3487-3502)Online publication date: 1-Apr-2021
https://dl.acm.org/doi/10.1007/s00521-020-05222-w
Khalil ALi SLin YLi HMa S(2020)A new expert system in prediction of lung cancer disease based on fuzzy soft setsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-020-04787-x24:18(14179-14207)Online publication date: 2-Mar-2020
https://dl.acm.org/doi/10.1007/s00500-020-04787-x
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Index Terms

A Hybrid Automatic System for the Diagnosis of Lung Cancer Based on Genetic Algorithm and Fuzzy Extreme Learning Machines
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
2. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Vagueness and fuzzy logic
  2. Machine learning
    1. Learning paradigms
    2. Machine learning approaches

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Journal of Medical Systems

Journal of Medical Systems Volume 36, Issue 2

Apr 2012

639 pages

ISSN:0148-5598

Issue’s Table of Contents

Copyright © Copyright © 2012 Springer Science+Business Media, LLC.

Publisher

Plenum Press

United States

Publication History

Published: 01 April 2012

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Cited By

Kiani RJin WSheng V(2024)Survey on extreme learning machines for outlier detectionMachine Language10.1007/s10994-023-06375-0113:8(5495-5531)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s10994-023-06375-0
Davoudi SAhmadi ADaliri M(2021)Frequency–amplitude coupling: a new approach for decoding of attended features in covert visual attention taskNeural Computing and Applications10.1007/s00521-020-05222-w33:8(3487-3502)Online publication date: 1-Apr-2021
https://dl.acm.org/doi/10.1007/s00521-020-05222-w
Khalil ALi SLin YLi HMa S(2020)A new expert system in prediction of lung cancer disease based on fuzzy soft setsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-020-04787-x24:18(14179-14207)Online publication date: 2-Mar-2020
https://dl.acm.org/doi/10.1007/s00500-020-04787-x
(2019)An automatic detection model of pulmonary nodules based on deep belief networkInternational Journal of Wireless and Mobile Computing10.5555/3337666.333766816:1(7-13)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.5555/3337666.3337668
(2018)Computational intelligence approaches for classification of medical dataNeurocomputing10.1016/j.neucom.2017.01.126276:C(2-22)Online publication date: 7-Feb-2018
https://dl.acm.org/doi/10.1016/j.neucom.2017.01.126
Zhang YWu JZhou CCai Z(2017)Instance cloned extreme learning machinePattern Recognition10.1016/j.patcog.2017.02.03668:C(52-65)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1016/j.patcog.2017.02.036
Venkatesan REr M(2016)A novel progressive learning technique for multi-class classificationNeurocomputing10.1016/j.neucom.2016.05.006207:C(310-321)Online publication date: 26-Sep-2016
https://dl.acm.org/doi/10.1016/j.neucom.2016.05.006
Ma ZDai Q(2016)Selected an Stacking ELMs for Time Series PredictionNeural Processing Letters10.1007/s11063-016-9499-944:3(831-856)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1007/s11063-016-9499-9
Huang GHuang GSong SYou K(2015)Trends in extreme learning machinesNeural Networks10.1016/j.neunet.2014.10.00161:C(32-48)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1016/j.neunet.2014.10.001
Taşcı EUğur A(2015)Shape and Texture Based Novel Features for Automated Juxtapleural Nodule Detection in Lung CTsJournal of Medical Systems10.1007/s10916-015-0231-539:5(1-13)Online publication date: 1-May-2015
https://dl.acm.org/doi/10.1007/s10916-015-0231-5
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