research-article

Benign and Malignant Solitary Pulmonary Nodules Classification Based on CNN and SVM

Authors:

Zhao HongyuanAuthors Info & Claims

ICMVA '18: Proceedings of the International Conference on Machine Vision and Applications

Pages 46 - 50

https://doi.org/10.1145/3220511.3220513

Published: 23 April 2018 Publication History

Abstract

In order to assist the doctors to diagnose lung cancer and improve the classification accuracy of benign and malignant pulmonary nodules, this paper proposes a novel intelligent diagnosis model which is aiming at CT imaging features of pulmonary nodules. Specifically, this model uses the convolutional neural network to extract the features of the pulmonary nodules, then uses the principal component analysis to reduce the dimension of the extracted features, and finally classifies the final features with particle swarm optimization optimized SVM. With regard to the pulmonary nodules extracted from the LIDC-IDRI database, 400 pulmonary nodules are used for training and 310 pulmonary nodules are used for testing, the classification accuracy rate is 91.94%. This model can provide objective, convenient and efficient auxiliary method for solving the classification problem of benign and malignant pulmonary nodules in medical images.

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Cited By

Ganguly KChakraborty N(2024)Identification of Lung Cancer Affected CT-Scan Images Using a Light-Weight Deep Learning ArchitectureProceedings of International Conference on Data, Electronics and Computing10.1007/978-981-97-6489-1_7(99-108)Online publication date: 8-Oct-2024
https://doi.org/10.1007/978-981-97-6489-1_7
M DM P(2022)Performance Evaluation of Convolutional Neural Network for Lung Cancer Detection2022 International Conference on Electronic Systems and Intelligent Computing (ICESIC)10.1109/ICESIC53714.2022.9783533(293-298)Online publication date: 22-Apr-2022
https://doi.org/10.1109/ICESIC53714.2022.9783533
Bilal ASun GLi YMazhar SLatif J(2022)Lung nodules detection using grey wolf optimization by weighted filters and classification using CNNJournal of the Chinese Institute of Engineers10.1080/02533839.2021.201252545:2(175-186)Online publication date: 6-Jan-2022
https://doi.org/10.1080/02533839.2021.2012525
Show More Cited By

Index Terms

Benign and Malignant Solitary Pulmonary Nodules Classification Based on CNN and SVM
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
    2. Health informatics
2. Computing methodologies
  1. Machine learning
    1. Learning settings
    2. Machine learning approaches

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cover image ACM Other conferences

ICMVA '18: Proceedings of the International Conference on Machine Vision and Applications

April 2018

81 pages

ISBN:9781450363815

DOI:10.1145/3220511

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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University of Canberra: University of Canberra

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 April 2018

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Funding Sources

Heilongjiang Province Natural Science Fund Project of China

Conference

ICMVA 2018

ICMVA 2018: International Conference on Machine Vision and Applications, ICMVA 2018

April 23 - 25, 2018

Singapore, Singapore

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Citations

Cited By

Ganguly KChakraborty N(2024)Identification of Lung Cancer Affected CT-Scan Images Using a Light-Weight Deep Learning ArchitectureProceedings of International Conference on Data, Electronics and Computing10.1007/978-981-97-6489-1_7(99-108)Online publication date: 8-Oct-2024
https://doi.org/10.1007/978-981-97-6489-1_7
M DM P(2022)Performance Evaluation of Convolutional Neural Network for Lung Cancer Detection2022 International Conference on Electronic Systems and Intelligent Computing (ICESIC)10.1109/ICESIC53714.2022.9783533(293-298)Online publication date: 22-Apr-2022
https://doi.org/10.1109/ICESIC53714.2022.9783533
Bilal ASun GLi YMazhar SLatif J(2022)Lung nodules detection using grey wolf optimization by weighted filters and classification using CNNJournal of the Chinese Institute of Engineers10.1080/02533839.2021.201252545:2(175-186)Online publication date: 6-Jan-2022
https://doi.org/10.1080/02533839.2021.2012525
Tiwari LRaja RAwasthi VMiri RSinha GAlkinani MPolat K(2021)Detection of lung nodule and cancer using novel Mask-3 FCM and TWEDLNN algorithmsMeasurement10.1016/j.measurement.2020.108882172(108882)Online publication date: Feb-2021
https://doi.org/10.1016/j.measurement.2020.108882
Silva FPereira TFrade JMendes JFreitas CHespanhol VCosta JCunha AOliveira H(2020)Pre-Training Autoencoder for Lung Nodule Malignancy Assessment Using CT ImagesApplied Sciences10.3390/app1021783710:21(7837)Online publication date: 5-Nov-2020
https://doi.org/10.3390/app10217837
Supriya SSubaji M(2020)Deep learning the features maps for automated tumor grading of lung nodule structures using convolutional neural networksIntelligent Decision Technologies10.3233/IDT-19008314:1(101-118)Online publication date: 30-Mar-2020
https://doi.org/10.3233/IDT-190083
Suresh SMohan S(2020)ROI-based feature learning for efficient true positive prediction using convolutional neural network for lung cancer diagnosisNeural Computing and Applications10.1007/s00521-020-04787-w32:20(15989-16009)Online publication date: 1-Oct-2020
https://dl.acm.org/doi/10.1007/s00521-020-04787-w

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