Computer aided diagnosis system for early lung cancer detection

Fatma Taher; Naoufel Werghi; Hussain Al-Ahmad

doi:10.1109/IWSSIP.2015.7313923

Computer aided diagnosis system for early lung cancer detection

Fatma Taher
, Naoufel Werghi
, Hussain Al-Ahmad

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Scopus citations

Abstract

In this paper, a new computer-aided diagnosis (CAD) system for early lung cancer detection based on the analysis of sputum color images is proposed. A set of features is extracted from the nuclei of the sputum cells after applying a region detection process. For training and testing the system we used two classification techniques: artificial neural network (ANN) and support vector machine (SVM) to increase the accuracy of the CAD system. The performance of the system was analyzed based on different criteria such as sensitivity, precision, specificity and accuracy. The evaluation was done by using Receiver Operating Characteristic (ROC) curve. The experimental results demonstrate the efficiency of SVM classifier over the ANN classifier with 97% of sensitivity and accuracy as well as a significant reduction in the number of false positive and false negative rates.

Original language	British English
Title of host publication	2015 22nd International Conference on Systems, Signals and Image Processing - Proceedings of IWSSIP 2015
Editors	Shahjahan Miah, Alena Uus, Panos Liatsis
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5-8
Number of pages	4
ISBN (Electronic)	9781467383530
DOIs	https://doi.org/10.1109/IWSSIP.2015.7313923
State	Published - 30 Oct 2015
Event	22nd International Conference on Systems, Signals and Image Processing, IWSSIP 2015 - London, United Kingdom Duration: 10 Sep 2015 → 12 Sep 2015

Publication series

Name	2015 22nd International Conference on Systems, Signals and Image Processing - Proceedings of IWSSIP 2015

Conference

Conference	22nd International Conference on Systems, Signals and Image Processing, IWSSIP 2015
Country/Territory	United Kingdom
City	London
Period	10/09/15 → 12/09/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Feature Extraction
Lung Cancer Diagnosis
Neural Network
Sputum Images
Support Vector Machine

Access to Document

10.1109/IWSSIP.2015.7313923

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Taher, F., Werghi, N., & Al-Ahmad, H. (2015). Computer aided diagnosis system for early lung cancer detection. In S. Miah, A. Uus, & P. Liatsis (Eds.), 2015 22nd International Conference on Systems, Signals and Image Processing - Proceedings of IWSSIP 2015 (pp. 5-8). Article 7313923 (2015 22nd International Conference on Systems, Signals and Image Processing - Proceedings of IWSSIP 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWSSIP.2015.7313923

Taher, Fatma ; Werghi, Naoufel ; Al-Ahmad, Hussain. / Computer aided diagnosis system for early lung cancer detection. 2015 22nd International Conference on Systems, Signals and Image Processing - Proceedings of IWSSIP 2015. editor / Shahjahan Miah ; Alena Uus ; Panos Liatsis. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 5-8 (2015 22nd International Conference on Systems, Signals and Image Processing - Proceedings of IWSSIP 2015).

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title = "Computer aided diagnosis system for early lung cancer detection",

abstract = "In this paper, a new computer-aided diagnosis (CAD) system for early lung cancer detection based on the analysis of sputum color images is proposed. A set of features is extracted from the nuclei of the sputum cells after applying a region detection process. For training and testing the system we used two classification techniques: artificial neural network (ANN) and support vector machine (SVM) to increase the accuracy of the CAD system. The performance of the system was analyzed based on different criteria such as sensitivity, precision, specificity and accuracy. The evaluation was done by using Receiver Operating Characteristic (ROC) curve. The experimental results demonstrate the efficiency of SVM classifier over the ANN classifier with 97\% of sensitivity and accuracy as well as a significant reduction in the number of false positive and false negative rates.",

keywords = "Feature Extraction, Lung Cancer Diagnosis, Neural Network, Sputum Images, Support Vector Machine",

author = "Fatma Taher and Naoufel Werghi and Hussain Al-Ahmad",

note = "Funding Information: Professor Allen T. Chwang at the University of Hong Kong is acknowledged. Without his enthusiasm, this paper will never be completed. This study is partially supported by National Science Council of R.O.C. under grant NSC80 - 0410 - E002 - 66. The authors are also very grateful to Miss Y.L. Chiang for her numerical assistance. Publisher Copyright: {\textcopyright} 2015 IEEE.; 22nd International Conference on Systems, Signals and Image Processing, IWSSIP 2015 ; Conference date: 10-09-2015 Through 12-09-2015",

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Taher, F, Werghi, N & Al-Ahmad, H 2015, Computer aided diagnosis system for early lung cancer detection. in S Miah, A Uus & P Liatsis (eds), 2015 22nd International Conference on Systems, Signals and Image Processing - Proceedings of IWSSIP 2015., 7313923, 2015 22nd International Conference on Systems, Signals and Image Processing - Proceedings of IWSSIP 2015, Institute of Electrical and Electronics Engineers Inc., pp. 5-8, 22nd International Conference on Systems, Signals and Image Processing, IWSSIP 2015, London, United Kingdom, 10/09/15. https://doi.org/10.1109/IWSSIP.2015.7313923

Computer aided diagnosis system for early lung cancer detection. / Taher, Fatma; Werghi, Naoufel; Al-Ahmad, Hussain.
2015 22nd International Conference on Systems, Signals and Image Processing - Proceedings of IWSSIP 2015. ed. / Shahjahan Miah; Alena Uus; Panos Liatsis. Institute of Electrical and Electronics Engineers Inc., 2015. p. 5-8 7313923 (2015 22nd International Conference on Systems, Signals and Image Processing - Proceedings of IWSSIP 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Werghi, Naoufel

AU - Al-Ahmad, Hussain

N1 - Funding Information: Professor Allen T. Chwang at the University of Hong Kong is acknowledged. Without his enthusiasm, this paper will never be completed. This study is partially supported by National Science Council of R.O.C. under grant NSC80 - 0410 - E002 - 66. The authors are also very grateful to Miss Y.L. Chiang for her numerical assistance. Publisher Copyright: © 2015 IEEE.

PY - 2015/10/30

Y1 - 2015/10/30

N2 - In this paper, a new computer-aided diagnosis (CAD) system for early lung cancer detection based on the analysis of sputum color images is proposed. A set of features is extracted from the nuclei of the sputum cells after applying a region detection process. For training and testing the system we used two classification techniques: artificial neural network (ANN) and support vector machine (SVM) to increase the accuracy of the CAD system. The performance of the system was analyzed based on different criteria such as sensitivity, precision, specificity and accuracy. The evaluation was done by using Receiver Operating Characteristic (ROC) curve. The experimental results demonstrate the efficiency of SVM classifier over the ANN classifier with 97% of sensitivity and accuracy as well as a significant reduction in the number of false positive and false negative rates.

AB - In this paper, a new computer-aided diagnosis (CAD) system for early lung cancer detection based on the analysis of sputum color images is proposed. A set of features is extracted from the nuclei of the sputum cells after applying a region detection process. For training and testing the system we used two classification techniques: artificial neural network (ANN) and support vector machine (SVM) to increase the accuracy of the CAD system. The performance of the system was analyzed based on different criteria such as sensitivity, precision, specificity and accuracy. The evaluation was done by using Receiver Operating Characteristic (ROC) curve. The experimental results demonstrate the efficiency of SVM classifier over the ANN classifier with 97% of sensitivity and accuracy as well as a significant reduction in the number of false positive and false negative rates.

KW - Feature Extraction

KW - Lung Cancer Diagnosis

KW - Neural Network

KW - Sputum Images

KW - Support Vector Machine

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M3 - Conference contribution

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SP - 5

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BT - 2015 22nd International Conference on Systems, Signals and Image Processing - Proceedings of IWSSIP 2015

A2 - Miah, Shahjahan

A2 - Uus, Alena

A2 - Liatsis, Panos

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 22nd International Conference on Systems, Signals and Image Processing, IWSSIP 2015

Y2 - 10 September 2015 through 12 September 2015

ER -

Taher F, Werghi N, Al-Ahmad H. Computer aided diagnosis system for early lung cancer detection. In Miah S, Uus A, Liatsis P, editors, 2015 22nd International Conference on Systems, Signals and Image Processing - Proceedings of IWSSIP 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 5-8. 7313923. (2015 22nd International Conference on Systems, Signals and Image Processing - Proceedings of IWSSIP 2015). doi: 10.1109/IWSSIP.2015.7313923

Computer aided diagnosis system for early lung cancer detection

Abstract

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UN SDGs

Keywords

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