research-article

Computer aided detection system for early cancerous pulmonary nodules by optimizing deep learning features

Authors:

Fatma E. Z. Abou-ChadiAuthors Info & Claims

ICSIE '19: Proceedings of the 8th International Conference on Software and Information Engineering

Pages 75 - 79

https://doi.org/10.1145/3328833.3328856

Published: 09 April 2019 Publication History

Abstract

In this paper, a deep learning technique for the early detection of pulmonary nodules from low dose CT (LDCT) images is proposed. The proposed technique is composed from four stages. Firstly, a preprocessing stage is applied to enhance image contrast of low dose images. Secondly, a transfer learning is utilized to extract deep learning features that describe the LDCT images. Thirdly, a genetic algorithm (GA) is learned on the extracted deep learning features using a training subset of the data to optimize the feature-set and select the most relevant features for cancerous nodules detection. Finally, a classification step of the selected features is performed using supported vector machines (SVM) to detect cancerous pulmonary nodules. Preliminary results on a number of 320 LDCT images acquired from 50 different subjects from the International Early Lung Cancer Action Project, I-ELCAP, online public lung image database has achieved a detection accuracy of 92.5%, sensitivity of 90%, and specificity of 95% Comparison results has shown the outstanding results of the proposed method. These preliminary results confirm the promising of our proposed method.

References

[1]

Kalra, M., Wang, G., and Orton, C. G. 2018. Radiomics in lung cancer: Its time is here. Medical physics, 45, 3, 997--1000.

[2]

Early Lung Cancer Action Program(ELCAP), available from: http://www.via.cornell.edu/lungdb.html.{Last accessed on Dec. 2018}

[3]

El-Regaily, S. A., Salem, M. A., Abdel Aziz, M. H., and Roushdy, M. I. 2018. Survey of Computer Aided Detection Systems for Lung Cancer in Computed Tomography. Current Medical Imaging Reviews, 14, 1, 3--18.

[4]

El-Baz, A., Beache, G.M., Gimel'farb, G., Suzuki, K., Okada, K., Elnakib, A., Soliman, A. and Abdollahi, B.2013. Computer-aided diagnosis systems for lung cancer: challenges and methodologies. International journal of biomedical imaging, 2013, 2013.

[5]

Li, L., Wu, Y., Yang, Y., Li, L. and Wu, B. 2018. June. A New Strategy to Detect Lung Cancer on CT Images. In 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) (pp. 716--722). IEEE.

[6]

Rattan, S., Kaur, S., Kansal, N. and Kaur, J. 2017. December. An optimized lung cancer classification system for computed tomography images. In 2017 Fourth International Conference on Image Information Processing (ICIIP), (pp. 1--6). IEEE.

[7]

Amer, H.M., Abou-Chadi, F.E., Kishk, S.S. and Obayya, M.I. 2018, May. A Computer-Aided Early Detection System of Pulmonary Nodules in CT Scan Images. In Proceedings of the 7th International Conference on Software and Information Engineering (pp. 81--86). ACM.

Digital Library

[8]

Jin, T., Cui, H., Zeng, S. and Wang, X. 2017, November. Learning Deep Spatial Lung Features by 3D Convolutional Neural Network for Early Cancer Detection. In Proceedings of the 2017 International Conference on Digital Image Computing: Techniques and Applications, (pp. 1--6). IEEE.

[9]

Shen, W., Zhou, M., Yang, F., Yu, D., Dong, D., Yang, C., Zang, Y. and Tian, J. 2017. Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recognition, 61, pp.663--673.

[10]

Dou, Q., Chen, H., Yu, L., Qin, J. and Heng, P.A. 2017. Multilevel contextual 3-d cnns for false positive reduction in pulmonary nodule detection. IEEE Transactions on Biomedical Engineering, 64(7), pp.1558--1567.

[11]

Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., Van Der Laak, J.A., Van Ginneken, B. and Sánchez, C.I. 2017. A survey on deep learning in medical image analysis. Medical image analysis, 42, pp.60--88.

[12]

Krizhevsky, A., Sutskever, I., Hinton, G. 2012. Imagenet classification with deep convolutional neural networks. In: Proceedings of the Advances in Neural Information Processing Systems. pp. 1097--1105.

Digital Library

[13]

Gao, M., Bagci, U., Lu, L., Wu, A., Buty, M., Shin, H.C., Roth, H., Papadakis, G.Z., Depeursinge, A., Summers, R.M. and Xu, Z. 2018. Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 6(1), pp. 1--6.

[14]

Andrews, H. C. (1976). Monochrome digital image enhancement. Applied optics, 15(2), pp. 495--503.

[15]

Miller, B.L. and Goldberg, D.E. 1995. Genetic algorithms, tournament selection, and the effects of noise, Complex systems, 9(3), pp.193--212.

[16]

Eiben, A.E. and Schippers, C.A. 1998. On evolutionary exploration and exploitation, Fundamenta Informaticae, 35(1--4), pp.35--50

Digital Library

[17]

Thierens, D. 2002. Adaptive mutation rate control schemes in genetic algorithms. In Proceedings of the 2002 Congress on Evolutionary Computation, CEC'02. (Vol. 1, pp. 980--985). IEEE.

[18]

Yerushalmy, J. (1947). Statistical problems in assessing methods of medical diagnosis, with special reference to X-ray techniques. Public Health Reports (1896-1970), 1432--1449.

Cited By

Ruprah TRegmi BJadhav SSingh S(2024)Early Stage Lung Cancer Detection Using Deep Learning2024 MIT Art, Design and Technology School of Computing International Conference (MITADTSoCiCon)10.1109/MITADTSoCiCon60330.2024.10575345(1-6)Online publication date: 25-Apr-2024
https://doi.org/10.1109/MITADTSoCiCon60330.2024.10575345
C. KVimala HJ. S(2024)A systematic review of artificial intelligence techniques for oral cancer detectionHealthcare Analytics10.1016/j.health.2024.1003045(100304)Online publication date: Jun-2024
https://doi.org/10.1016/j.health.2024.100304
Kudva AKudva VPatil VMohan NShenoy PRai A(2024)Applications of Artificial Intelligence in the Analysis of Images of the Oral Cavity for Cancer DetectionBiomedical Imaging10.1007/978-981-97-5345-1_6(157-169)Online publication date: 27-Sep-2024
https://doi.org/10.1007/978-981-97-5345-1_6

Index Terms

Computer aided detection system for early cancerous pulmonary nodules by optimizing deep learning features
1. Applied computing
  1. Physical sciences and engineering
    1. Engineering
      1. Computer-aided design

Recommendations

Review on Deep Learning Methods Used for Computer-aided Lung Cancer Detection and Diagnosis
ACAI '19: Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence

Cancer is one of the diseases with high mortality rates in the 21st century, with lung cancer being the first in all cancer morbidity and mortality rates. In recent years, with the large rise of data and artificial intelligence researches, the auxiliary ...
A Computer-Aided Early Detection System of Pulmonary Nodules in CT Scan Images
ICSIE '18: Proceedings of the 7th International Conference on Software and Information Engineering

In the present paper, computer-aided system for the early detection of pulmonary nodules in Computed Tomography (CT) scan images is developed where pulmonary nodules are one of the critical notifications to identify lung cancer. The proposed system ...
A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning
Abstract
Lung cancer has one of the highest mortalities of all cancers. According to the National Lung Screening Trial, patients who underwent low-dose computed tomography (CT) scanning once a year for 3 years showed a 20% decline in lung cancer ...
Highlights
- We introduce the development process of deep learning and several important parts of lung nodule CAD system.
- We summarize the performance of the selected work in lung nodule detection, diagnosis and retrieval.
- We review and compare ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICSIE '19: Proceedings of the 8th International Conference on Software and Information Engineering

April 2019

276 pages

ISBN:9781450361057

DOI:10.1145/3328833

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 April 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICSIE '19

ICSIE '19: 2019 8th International Conference on Software and Information Engineering

April 9 - 12, 2019

Cairo, Egypt

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
122
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)0

Reflects downloads up to 17 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Ruprah TRegmi BJadhav SSingh S(2024)Early Stage Lung Cancer Detection Using Deep Learning2024 MIT Art, Design and Technology School of Computing International Conference (MITADTSoCiCon)10.1109/MITADTSoCiCon60330.2024.10575345(1-6)Online publication date: 25-Apr-2024
https://doi.org/10.1109/MITADTSoCiCon60330.2024.10575345
C. KVimala HJ. S(2024)A systematic review of artificial intelligence techniques for oral cancer detectionHealthcare Analytics10.1016/j.health.2024.1003045(100304)Online publication date: Jun-2024
https://doi.org/10.1016/j.health.2024.100304
Kudva AKudva VPatil VMohan NShenoy PRai A(2024)Applications of Artificial Intelligence in the Analysis of Images of the Oral Cavity for Cancer DetectionBiomedical Imaging10.1007/978-981-97-5345-1_6(157-169)Online publication date: 27-Sep-2024
https://doi.org/10.1007/978-981-97-5345-1_6

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents