Volume 32, Issue 1 (April 2021)                   Studies in Medical Sciences 2021, 32(1): 67-81 | Back to browse issues page

XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Majidpourkhoei R, Alilou M, Majidzadeh K, BabazadehSangar A. INTRODUCING AN INTELLIGENT FRAMEWORK FOR DETECTION OF SUSPECTED LUNG NODULES. Studies in Medical Sciences 2021; 32 (1) :67-81
URL: http://umj.umsu.ac.ir/article-1-5385-en.html
Assistant Professor, Department of Computer engineering, Urmia Branch, Islamic Azad University, Urmia, Iran. (Corresponding Author) , Mehdi.a.m.i.p@gmail.com
Abstract:   (2321 Views)
Background & Aims: One of the symptoms of lung cancer, which is one of the deadliest cancers, is the lung nodules. It is very difficult to detect these tiny nodules on CT scans of the lungs with the naked eye. Therefore, intelligent systems or computer-aided detection (CAD) systems can assist a radiologist in detecting, locating, and evaluating the quality of lung nodules. The most important challenge of existing intelligent systems is the balanced improvement of accuracy, sensitivity, specificity, and reduction of false positive rate (FPr), and also the complexity of these systems has reduced the efficiency and speed of execution. Therefore, the purpose of this study was to provide an agile framework and optimize the challenge.
Materials & Methods: One of the new subfields of artificial intelligence is the deep learning and orientation of CNN networks, which has been widely used in the analysis of medical images in recent years. In this research, an innovative network based on CNN networks of LeNet type is proposed to extract image features as well as image classification. The used dataset is a subset of 7072 image pieces derived from the LIDC-IDRI standard dataset. The size of nodules of these images, which are used to train and validate the network, are 1 to 4 mm.
Results: The training and validation processes of this network were performed with a computer device (configurations 2.4GHz Core i5 processor, 8GB of memory, and Intel Graphics 520) in five hours and eleven minutes and the accuracy, sensitivity, and specificity are 91.1%, 85.3% and 92.8%, respectively.
Conclusion: Based on the standard basis of the proposed model and also the use of valid database images to measure the network and compare with previous works, the results establish a good balance between evaluation criteria, and with faster implementation gain the necessary capability for real time applications.
Full-Text [PDF 1221 kb]   (1276 Downloads)    
Type of Study: Research | Subject: General

References
1. American Cancer Society, Key Statistics for Lung Cancer, 2020,
2. Available from https://www.cancer.org/cancer/lung-cancer/about/key-statistics.html. [URL]
3. A.C. Society, Cancer Facts and Figures, 2017. Available from http://www.cancer.org/acs/groups/content/@editorial/documents/document/acspc-044552.pdf. [URL]
4. Hussein S, Gillies R, Cao K, Song Q, Bagci U. TumorNet: Lung nodule characterization using multi-view Convolutional Neural Network with Gaussian Process. In2017 IEEE 14th International Symposium on Biomedical Imaging. 2017. Song Q, Zhao L, Luo X, Dou X. Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images. J Healthc Eng 2017; 2017:1-7. Available from: http://dx.doi.org/10.1155/2017/8314740 [DOI:10.1155/2017/8314740] [PMID] [PMCID]
5. Sun W, Tseng T-LB, Qian W, Zhang J, Saltzstein EC, Zheng B, et al. Using multiscale texture and density features for near-term breast cancer risk analysis. Med Phys 2015;42(6Part1):2853-62. Available from: http://dx.doi.org/10.1118/1.4919772 [DOI:10.1118/1.4919772] [PMID] [PMCID]
6. Sun W, Huang X, Tseng T-L, Zhang J, Qian W. Computerized lung cancer malignancy level analysis using 3D texture features. Tourassi GD, Armato SG, editors. Medical Imaging 2016: Computer Aided Diagnosis. SPIE; 2016. Available from: http://dx.doi.org/10.1117/12.2216329 [DOI:10.1117/12.2216329]
7. Hossain MS, Muhammad G. Cloud-Based Collaborative Media Service Framework for HealthCare. Int J Distrib Sens Netw 2014;10(3):858712. Available from: http://dx.doi.org/10.1155/2014/858712 [DOI:10.1155/2014/858712]
8. Amin SU, Alsulaiman M, Muhammad G, Mekhtiche MA, Shamim Hossain M. Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion. Future Generat Comput Syst 2019;101:542-54. Available from: http://dx.doi.org/10.1016/j.future.2019.06.027 [DOI:10.1016/j.future.2019.06.027]
9. De Carvalho Filho AO, Silva AC, de Paiva AC, Nunes RA, Gattass M. Classification of patterns of benignity and malignancy based on CT using topology-based phylogenetic diversity index and convolutional neural network. Pattern Recogn 2018;81:200-12. Available from: http://dx.doi.org/10.1016/j.patcog.2018.03.032 [DOI:10.1016/j.patcog.2018.03.032]
10. Wang Y, Qiu Y, Thai T, Moore K, Liu H, Zheng B. A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images. Comput Meth Programs Biomed 2017;144:97-104. Available from: http://dx.doi.org/10.1016/j.cmpb.2017.03.017 [DOI:10.1016/j.cmpb.2017.03.017] [PMID] [PMCID]
11. Monkam P, Qi S, Ma H, Gao W, Yao Y, Qian W. Detection and Classification of Pulmonary Nodules Using Convolutional Neural Networks: A Survey. IEEE Access 2019;7:78075-91. Available from: http://dx.doi.org/10.1109/access.2019.2920980 [DOI:10.1109/ACCESS.2019.2920980]
12. Saba T. Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges. J Infect Public Heal2020;13(9):1274-89. Available from: http://dx.doi.org/10.1016/j.jiph.2020.06.033 [DOI:10.1016/j.jiph.2020.06.033] [PMID]
13. Affonso C, Sassi RJ, Barreiros RM. Biological image classification using rough-fuzzy artificial neural network. Expert Syst Appl 2015;42(24):9482-8. Available from: http://dx.doi.org/10.1016/j.eswa.2015.07.075 [DOI:10.1016/j.eswa.2015.07.075]
14. Yu-Jen Chen Y-J, Hua K-L, Hsu C-H, Cheng W-H, Hidayati SC. Computer-aided classification of lung nodules on computed tomography images via deep learning technique. OncoTargets Ther 2015; 8:2015-22. Available from: http://dx.doi.org/10.2147/ott.s80733 [DOI:10.2147/OTT.S80733] [PMID] [PMCID]
15. Greenspan H, van Ginneken B, Summers RM. Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique. IEEE Trans Med Imag; 2016;35(5):1153-9. Available from: http://dx.doi.org/10.1109/tmi.2016.2553401 [DOI:10.1109/TMI.2016.2553401]
16. Ongsulee P. Artificial intelligence, machine learning and deep learning. 2017 15th Int Conf ICT Knowl (ICT&KE). IEEE; 2017 Nov; Available from: http://dx.doi.org/10.1109/ictke.2017.8259629 [DOI:10.1109/ICTKE.2017.8259629]
17. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017;42:60-88. Available from: http://dx.doi.org/10.1016/j.media.2017.07.005 [DOI:10.1016/j.media.2017.07.005] [PMID]
18. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 2017 ;60(6):84-90. Available from: http://dx.doi.org/10.1145/3065386 [DOI:10.1145/3065386]
19. Naik A, Edla DR. Lung Nodule Classification on Computed Tomography Images Using Deep Learning. Wireless Personal Communications 2020; 116: 655-90. [DOI:10.1007/s11277-020-07732-1]
20. Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE; 1998;86(11):2278-324. Available from: http://dx.doi.org/10.1109/5.726791 [DOI:10.1109/5.726791]
21. Coşkun m, yildirim ö, uçar a, demir y, et al. an overview of popular deep learning methods. Eur J Tech 2017; 7(2):165-76. Available from: http://dx.doi.org/10.23884/ejt.2017.7.2.11 [DOI:10.23884/ejt.2017.7.2.11]
22. Wang S, Zhou M, Liu Z, Liu Z, Gu D, Zang Y, et al. Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation. Med Image Anal 2017;40:172-83. Available from: http://dx.doi.org/10.1016/j.media.2017.06.014 [DOI:10.1016/j.media.2017.06.014] [PMID] [PMCID]
23. Szegedy C, Wei Liu, Yangqing Jia, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition 2015. p. 1-9.; Available from: http://dx.doi.org/10.1109/cvpr.2015.7298594 [DOI:10.1109/CVPR.2015.7298594]
24. He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition 2015 p. 1-9.Available from: http://dx.doi.org/10.1109/cvpr.2016.90 [DOI:10.1109/CVPR.2016.90] [PMID]
25. Hu J, Shen L, Albanie S, Sun G, Wu E. Squeeze-and-Excitation Networks. IEEE Trans Pattern Anal Mach Intell 2020 ;42(8):2011-23. Available from: http://dx.doi.org/10.1109/tpami.2019.2913372 [DOI:10.1109/TPAMI.2019.2913372] [PMID]
26. He K, Zhang X, Ren S, Sun J. Identity Mappings in Deep Residual Networks. Lect Notes Comput Sci 2016;630-45. Available from: http://dx.doi.org/10.1007/978-3-319-46493-0_38 [DOI:10.1007/978-3-319-46493-0_38]
27. Alilou M, Kovalev V, Snezhko E, Taimouri V. a comprehensive framework for automatic detection of pulmonary nodules in lung ct images. Image Analysis & Stereology 2014;33(1):13. Available from: http://dx.doi.org/10.5566/ias.v33.p13-27 [DOI:10.5566/ias.v33.p13-27]
28. Wang S, Zhou M, Liu Z, Liu Z, Gu D, Zang Y, et al. Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation. Med Image Anal 2017;40:172-83. Available from: http://dx.doi.org/10.1016/j.media.2017.06.014 [DOI:10.1016/j.media.2017.06.014] [PMID] [PMCID]
29. Lee SLA, Kouzani AZ, Hu EJ. Automated detection of lung nodules in computed tomography images: a review. Machine vision and applications 2010 ;23(1):151-63. Available from: http://dx.doi.org/10.1007/s00138-010-0271-2 [DOI:10.1007/s00138-010-0271-2]
30. Armato SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, et al. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans. Med Phys 2011 ;38(2):915-31. Available from: http://dx.doi.org/10.1118/1.3528204 [DOI:10.1118/1.3528204] [PMID] [PMCID]
31. Ji S, Zhang C, Xu A, Shi Y, Duan Y. 3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images. Rem Sens 2018;10(2):75. Available from: http://dx.doi.org/10.3390/rs10010075 [DOI:10.3390/rs10010075]
32. Tajbakhsh N, Suzuki K. Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: MTANNs vs. CNNs. Pattern Recogn 2017;63:476-86. Available from: http://dx.doi.org/10.1016/j.patcog.2016.09.029 [DOI:10.1016/j.patcog.2016.09.029]
33. El-Regaily SA, Salem MAM, Abdel Aziz MH, Roushdy MI. Multi-view Convolutional Neural Network for lung nodule false positive reduction. Expert Syst Appl 2020;162:113017. Available from: http://dx.doi.org/10.1016/j.eswa.2019.113017 [DOI:10.1016/j.eswa.2019.113017]
34. Bengio Y. Deep learning of representations for unsupervised and transfer learning. In Proceedings of ICML workshop on unsupervised and transfer learning 2012 Jun 27 (pp. 17-36). [Google Scholar]
35. Goldberg-Zimring D, Achiron A, Miron S, Faibel M, Azhari H. Automated Detection and Characterization of Multiple Sclerosis Lesions in Brain MR Images. Magn Reson Imag 1998;16(3):311-8. Available from: http://dx.doi.org/10.1016/s0730-725x(97)00300-7 [DOI:10.1016/S0730-725X(97)00300-7]
36. Kumar D, Wong A, Clausi DA. Lung Nodule Classification Using Deep Features in CT Images. 2015 12th Conf Comput Robot Vis. IEEE; 2015 Jun; Available from: http://dx.doi.org/10.1109/crv.2015.25 [DOI:10.1109/CRV.2015.25]
37. Setio AAA, Ciompi F, Litjens G, Gerke P, Jacobs C, van Riel SJ, et al. Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks. IEEE Trans Med Imag 2016;35(5):1160-9. Available from: http://dx.doi.org/10.1109/tmi.2016.2536809 [DOI:10.1109/TMI.2016.2536809] [PMID]
38. Shen W, Zhou M, Yang F, Yang C, Tian J. Multi-scale Convolutional Neural Networks for Lung Nodule Classification. Inform Process Med Imag 2015;588-99. Available from: http://dx.doi.org/10.1007/978-3-319-19992-4_46 [DOI:10.1007/978-3-319-19992-4_46] [PMID]
39. Shen W, Zhou M, Yang F, Yu D, Dong D, Yang C, et al. Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification. Pattern Recogn 2017;61:663-73. Available from: http://dx.doi.org/10.1016/j.patcog.2016.05.029 [DOI:10.1016/j.patcog.2016.05.029]
40. Jiang H, Ma H, Qian W, Gao M, Li Y. An Automatic Detection System of Lung Nodule Based on Multigroup Patch-Based Deep Learning Network. IEEE J Biomed Health 2018;22(4):1227-37. Available from: http://dx.doi.org/10.1109/jbhi.2017.2725903 [DOI:10.1109/JBHI.2017.2725903] [PMID]
41. Gruetzemacher R, Gupta A. Using deep learning for pulmonary nodule detection & diagnosis. 22 Americas Conf Inform Syst, 2016; San Diego. [Google Scholar]
42. Ypsilantis PP, Montana G. Recurrent convolutional networks for pulmonary nodule detection in CT imaging. arXiv preprint arXiv:1609.09143 2016; Sep 28. [Google Scholar]
43. Alakwaa W, Nassef M, Badr A. Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN). Lung Cancer 2017;8(8):409. [DOI:10.14569/IJACSA.2017.080853]
44. Wang B, Qi G, Tang S, Zhang L, Deng L, Zhang Y. Automated Pulmonary Nodule Detection: High Sensitivity with Few Candidates. Lect Notes Comput Sci. Springer International Publishing; 2018;759-67. Available from: http://dx.doi.org/10.1007/978-3-030-00934-2_84 [DOI:10.1007/978-3-030-00934-2_84]
45. Xie H, Yang D, Sun N, Chen Z, Zhang Y. Automated pulmonary nodule detection in CT images using deep convolutional neural networks. Pattern Recogn 2019;85:109-19. Available from: http://dx.doi.org/10.1016/j.patcog.2018.07.031 [DOI:10.1016/j.patcog.2018.07.031]
46. Silveira M, Nascimento J, Marques J. Automatic segmentation of the lungs using robust level sets. 29th Annu Int Conf IEEE Eng Med Biol Soc. IEEE; 2007 Aug; Available from: http://dx.doi.org/10.1109/iembs.2007.4353317 [DOI:10.1109/IEMBS.2007.4353317] [PMID]
47. Hassanpour H, Yousefian H, Zehtabi A. Pixon-Based Image Segmentation. Image Segmentation. InTech; 2011 Apr 19; Available from: http://dx.doi.org/10.5772/15941 [DOI:10.5772/15941]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2024 CC BY-NC 4.0 | Studies in Medical Sciences

Designed & Developed by : Yektaweb