research-article

Analysis of a feature-deselective neuroevolution classifier (FD-NEAT) in a computer-aided lung nodule detection system for CT images

Authors:

Jan CornelisAuthors Info & Claims

GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation

Pages 539 - 546

https://doi.org/10.1145/2330784.2330869

Published: 07 July 2012 Publication History

Abstract

Systems for Computer-Aided Detection (CAD), specifically for lung nodule detection received increasing attention in recent years. This is in tandem with the observation that patients who are diagnosed with early stage lung cancer and who undergo curative resection have a much better prognosis. In this paper, we analyze the performance of a novel feature-deselective neuroevolution method called FD-NEAT to retain relevant features derived from CT images and evolve neural networks that perform well for combined feature selection and classification. Network performance is analyzed based on radiologists' ratings of various lung nodule characteristics defined in the LIDC database. The analysis shows that the FD-NEAT classifier relates well with the radiologists' perception in almost all the defined nodule characteristics, and shows that FD-NEAT evolves networks that are less complex than the fixed-topology ANN in terms of number of connections.

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Cited By

Calimeri FMarzullo AStamile CTerracina G(2018)Blood Vessel Segmentation in Retinal Fundus Images Using Hypercube NeuroEvolution of Augmenting Topologies (HyperNEAT)Quantifying and Processing Biomedical and Behavioral Signals10.1007/978-3-319-95095-2_17(173-183)Online publication date: 18-Aug-2018
https://doi.org/10.1007/978-3-319-95095-2_17
Papavasileiou EJansen B(2017)An investigation of topological choices in FS-NEAT and FD-NEAT on XOR-based problems of increased complexityProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3067695.3082497(1431-1434)Online publication date: 15-Jul-2017
https://dl.acm.org/doi/10.1145/3067695.3082497
Papavasileiou EJansen B(2017)The importance of the activation function in NeuroEvolution with FS-NEAT and FD-NEAT2017 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2017.8285328(1-7)Online publication date: Nov-2017
https://doi.org/10.1109/SSCI.2017.8285328
Show More Cited By

Index Terms

Analysis of a feature-deselective neuroevolution classifier (FD-NEAT) in a computer-aided lung nodule detection system for CT images
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Neural networks
2. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Conferences

GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation

July 2012

1586 pages

ISBN:9781450311786

DOI:10.1145/2330784

Editor:
Terence Soule
University of Idaho, USA
,
General Chair:
Jason H. Moore
Dartmouth College, USA

Copyright © 2012 ACM.

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Published: 07 July 2012

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GECCO '12

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GECCO '12: Genetic and Evolutionary Computation Conference

July 7 - 11, 2012

Pennsylvania, Philadelphia, USA

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Cited By

Calimeri FMarzullo AStamile CTerracina G(2018)Blood Vessel Segmentation in Retinal Fundus Images Using Hypercube NeuroEvolution of Augmenting Topologies (HyperNEAT)Quantifying and Processing Biomedical and Behavioral Signals10.1007/978-3-319-95095-2_17(173-183)Online publication date: 18-Aug-2018
https://doi.org/10.1007/978-3-319-95095-2_17
Papavasileiou EJansen B(2017)An investigation of topological choices in FS-NEAT and FD-NEAT on XOR-based problems of increased complexityProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3067695.3082497(1431-1434)Online publication date: 15-Jul-2017
https://dl.acm.org/doi/10.1145/3067695.3082497
Papavasileiou EJansen B(2017)The importance of the activation function in NeuroEvolution with FS-NEAT and FD-NEAT2017 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2017.8285328(1-7)Online publication date: Nov-2017
https://doi.org/10.1109/SSCI.2017.8285328
Papavasileiou EJansen B(2016)A comparison between FS-NEAT and FD-NEAT and an investigation of different initial topologies for a classification task with irrelevant features2016 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2016.7850127(1-8)Online publication date: Dec-2016
https://doi.org/10.1109/SSCI.2016.7850127
Loscalzo SWright RYu L(2015)Predictive feature selection for genetic policy searchAutonomous Agents and Multi-Agent Systems10.1007/s10458-014-9268-y29:5(754-786)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1007/s10458-014-9268-y

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