research-article

Lung Cancer Detection with 3D Ensemble Convolution Neural Network

Authors:

Haichuan YuAuthors Info & Claims

CSAI '19: Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence

Pages 64 - 70

https://doi.org/10.1145/3374587.3374588

Published: 04 March 2020 Publication History

Abstract

Lung cancer, with the highest morbidity and mortality in 21th century, is difficult to cure because people often fail to detect it in time. With the blossom of artificial intelligence and big data research, people seem to see the dawn of solving this problem. Computer aided diagnosis (CAD) system of lung cancer based on deep learning techniques have been a popular research topic, which aims at utilize the computer system to analyze the possibility of cancer which is beneficial to the treatment in early stages and increase the possibility of recovery. Here we propose an integrated network system based on 3D Convolutional Neural Network (CNN), consisting mainly of two different 3D CNNs. And finally we reached 82.89% accuracy by combining 3D CNNs' feature outputs and using the softmax activation function to get the final disease probability. Also, we modified the decision making method to make the final result tending to get the negative output to decrease the false positive rate and the true positive rate reached 90.38% eventually.

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Cited By

Sun LDong YXu SFeng XFan X(2023)Predicting Multi-Gene Mutation Based on Lung Cancer CT Images and Mut-SeResNetApplied Sciences10.3390/app1303192113:3(1921)Online publication date: 2-Feb-2023
https://doi.org/10.3390/app13031921
R P PK S AShetty ARao ASooda A(2023)Performance Evaluation of Machine Learning Algorithms for Lung Nodule Detection using Multi-Modal Imaging - A Hybrid Approach2023 2nd International Conference on Edge Computing and Applications (ICECAA)10.1109/ICECAA58104.2023.10212387(1115-1123)Online publication date: 19-Jul-2023
https://doi.org/10.1109/ICECAA58104.2023.10212387

Index Terms

Lung Cancer Detection with 3D Ensemble Convolution Neural Network
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition

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cover image ACM Other conferences

CSAI '19: Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence

December 2019

370 pages

ISBN:9781450376273

DOI:10.1145/3374587

Copyright © 2019 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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Shenzhen University: Shenzhen University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 March 2020

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CSAI2019

CSAI2019: 2019 3rd International Conference on Computer Science and Artificial Intelligence

December 6 - 8, 2019

IL, Normal, USA

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Cited By

Sun LDong YXu SFeng XFan X(2023)Predicting Multi-Gene Mutation Based on Lung Cancer CT Images and Mut-SeResNetApplied Sciences10.3390/app1303192113:3(1921)Online publication date: 2-Feb-2023
https://doi.org/10.3390/app13031921
R P PK S AShetty ARao ASooda A(2023)Performance Evaluation of Machine Learning Algorithms for Lung Nodule Detection using Multi-Modal Imaging - A Hybrid Approach2023 2nd International Conference on Edge Computing and Applications (ICECAA)10.1109/ICECAA58104.2023.10212387(1115-1123)Online publication date: 19-Jul-2023
https://doi.org/10.1109/ICECAA58104.2023.10212387

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