دوره 20، شماره 3 - ( 10-1402 )                   جلد 20 شماره 3 صفحات 46-27 | برگشت به فهرست نسخه ها

XML English Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Mohammadian Takaloo V, Hashemzadeh M, Ghavidel Neycharan J. CoviX-Net: A Deep Learning-based System for Diagnosis and Differentiation of Covid-19 Infection and Pneumonia in Chest Radiography Images. JSDP 2023; 20 (3) : 3
URL: http://jsdp.rcisp.ac.ir/article-1-1238-fa.html
محمدیان وحید، هاشم زاده مهدی، قویدل نیچران جلیل. CoviX-Net: سامانه مبتنی بر یادگیری عمیق برای تشخیص و تمایز عفونت کوید-19 و ذات‌الریه در تصاویر رادیوگرافی قفسه سینه. پردازش علائم و داده‌ها. 1402; 20 (3) :27-46

URL: http://jsdp.rcisp.ac.ir/article-1-1238-fa.html

CoviX-Net: سامانه مبتنی بر یادگیری عمیق برای تشخیص و تمایز عفونت کوید-19 و ذات‌الریه در تصاویر رادیوگرافی قفسه سینه
وحید محمدیان ، مهدی هاشم زاده* ، جلیل قویدل نیچران
دانشگاه شهید مدنی آذربایجان
چکیده:   (471 مشاهده)
در این پژوهش، سامانه CoviX-Net مبتنی بر یادگیری عمیق برای تشخیص و تمایز بیماری کوید-19 و انواع ذات‌الریه از روی تصاویر رادیوگرافی سینه ارائه می‌شود. معماری مدل یادگیری CoviX-Net، بر اساس معماری اِکسپشن چند لایه و متناسب با کاربرد مورد هدف طراحی شده است. در این سامانه، از یادگیری انتقالی برای رفع مشکل کمبود داده آموزشی استفاده می‌شود. همچنین برای فراهم نمودن داده آموزشی کافی، یک پایگاه تصاویر جامع با بهره‌گیری مناسب از دو منبع مختلف از تصاویر قفسه سینه ایجاد شده است. برای جلوگیری از مشکل بیش‌برازش، فنون افزایش داده، تَنزلِ وزن و تنظیم ‌کننده‌های L2 استفاده شده است. نتایج ارزیابی‌ها نشان می‌دهد دقت CoviX-Net در حالت سه طبقه (کوید-19، ذات‌الریه و ریه طبیعی) %25/99، و در حالت چهار طبقه (کوید-19، ذات‌الریه باکتریایی، ذات‌الریه ویروسی و ریه طبیعی) %95 است که در مقایسه با دیگر روش‌های طبقه‌بندی مبتنی بر یادگیری عمیق با ساختار مشابه، بهبود دقت %5 و در مقایسه با روش مبتنی بر یادگیری عمیق انتقالی موازی، با ساختار پیچیده، بهبود دقت حدود نیم درصد را دارد. کلیه کدهای پیاده‌سازی CoviX-Net و مجموعه تصاویر گردآوری شده در دسترس عموم پژوهشگران قرار گرفته است.
شماره‌ی مقاله: 3
واژه‌های کلیدی: بینایی ماشین، یادگیری عمیق، تشخیص کوید-19، ذات‌الریه، تصاویر رادیوگرافی قفسه سینه
متن کامل [PDF 1983 kb]   (105 دریافت)    
نوع مطالعه: پژوهشي | موضوع مقاله: مقالات پردازش تصویر
دریافت: 1400/3/12 | پذیرش: 1402/9/20 | انتشار: 1402/10/24 | انتشار الکترونیک: 1402/10/24
فهرست منابع
1. [1] C. Sohrabi et al., "World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19)," International Journal of Surgery, vol. 76, pp. 71-76, 2020/04/01/ 2020, doi: https://doi.org/10.1016/j.ijsu.2020.02.034 [DOI:10.1016/j.ijsu.2020.02.034.] [PMID] []
2. [2] C.-C. Lai, T.-P. Shih, W.-C. Ko, H.-J. Tang, and P.-R. Hsueh, "Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges," International Journal of Antimicrobial Agents, vol. 55, no. 3, p. 105924, 2020/03/01/ 2020, doi: https://doi.org/10.1016/j.ijantimicag.2020.105924 [DOI:10.1016/j.ijantimicag.2020.105924.] [PMID] []
3. [3] H. A. Rothan and S. N. Byrareddy, "The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak," Journal of Autoimmunity, vol. 109, p. 102433, 2020/05/01/ 2020, doi: https://doi.org/10.1016/j.jaut.2020.102433 [DOI:10.1016/j.jaut.2020.102433.] [PMID] []
4. [4] R. M. Pereira, D. Bertolini, L. O. Teixeira, C. N. Silla, and Y. M. G. Costa, "COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios," Computer Methods and Programs in Biomedicine, vol. 194, p. 105532, 2020/10/01/ 2020, doi: https://doi.org/10.1016/j.cmpb.2020.105532 [DOI:10.1016/j.cmpb.2020.105532.] [PMID] []
5. [5] T. Franquet, "Imaging of Pulmonary Viral Pneumonia," Radiology, vol. 260, no. 1, pp. 18-39, 2011, doi: 10.1148/radiol.11092149. [DOI:10.1148/radiol.11092149] [PMID]
6. [6] S. Latif et al., "Leveraging Data Science to Combat COVID-19: A Comprehensive Review," IEEE Transactions on Artificial Intelligence, vol. 1, no. 1, pp. 85-103, 2020, doi: 10.1109/TAI.2020.3020521. [DOI:10.1109/TAI.2020.3020521] [PMID] []
7. [7] G. D. Rubin et al., "The Role of Chest Imaging in Patient Management During the COVID-19 Pandemic: A Multinational Consensus Statement From the Fleischner Society," Chest, vol. 158, no. 1, pp. 106-116, 2020/07/01/ 2020, doi: https://doi.org/10.1016/j.chest.2020.04.003 [DOI:10.1016/j.chest.2020.04.003.] [PMID] []
8. [8] C. Long et al., "Diagnosis of the Coronavirus disease (COVID-19): rRT-PCR or CT?," European Journal of Radiology, vol. 126, p. 108961, 2020/05/01/ 2020, doi: https://doi.org/10.1016/j.ejrad.2020.108961 [DOI:10.1016/j.ejrad.2020.108961.] [PMID] []
9. [9] F. Chollet, "Xception: Deep learning with depthwise separable convolutions," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 1251-1258.
10. [10] A. Shoeibi et al., "Automated detection and forecasting of covid-19 using deep learning techniques: A review," arXiv preprint arXiv:2007.10785, 2020.
11. [11] R. Karthik, R. Menaka, M. Hariharan, and G. S. Kathiresan, "AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions," IRBM, 2021/07/26/ 2021, doi: https://doi.org/10.1016/j.irbm.2021.07.002 [DOI:10.1016/j.irbm.2021.07.002.] [PMID] []
12. [12] M. S. Yang, "A survey of fuzzy clustering," Mathematical and Computer Modelling, vol. 18, no. 11, pp. 1-16, 1993/12/01/ 1993, doi: https://doi.org/10.1016/0895-7177(93)90202-A [DOI:10.1016/0895-7177(93)90202-A.]
13. [13] V. Badrinarayanan, A. Kendall, and R. Cipolla, "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 12, pp. 2481-2495, 2017, doi: 10.1109/TPAMI.2016.2644615. [DOI:10.1109/TPAMI.2016.2644615] [PMID]
14. [14] S. H. Gao, M. M. Cheng, K. Zhao, X. Y. Zhang, M. H. Yang, and P. Torr, "Res2Net: A New Multi-Scale Backbone Architecture," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 2, pp. 652-662, 2021, doi: 10.1109/TPAMI.2019.2938758. [DOI:10.1109/TPAMI.2019.2938758] [PMID]
15. [15] O. Ronneberger, P. Fischer, and T. Brox, "U-Net: Convolutional Networks for Biomedical Image Segmentation," Cham, 2015: Springer International Publishing, in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, pp. 234-241.
16. [16] K. O Shea and R. Nash, "An Introduction to Convolutional Neural Networks," ArXiv, vol. abs/1511.08458, 2015.
17. [17] Z.-W. Yuan and J. Zhang, Feature extraction and image retrieval based on AlexNet (Eighth International Conference on Digital Image Processing (ICDIP 2016)). SPIE, 2016.
18. [18] S. Tammina, "Transfer learning using VGG-16 with deep convolutional neural network for classifying images," International Journal of Scientific and Research Publications, vol. 9, no. 10, pp. 143-150, 2019.
19. [19] P. Aswathy, Siddhartha, and D. Mishra, "Deep GoogLeNet Features for Visual Object Tracking," in 2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS), 1-2 Dec. 2018 2018, pp. 60-66, doi: 10.1109/ICIINFS.2018.8721317. [DOI:10.1109/ICIINFS.2018.8721317] [PMID]
20. [20] K. Zhang, Y. Guo, X. Wang, J. Yuan, and Q. Ding, "Multiple Feature Reweight DenseNet for Image Classification," IEEE Access, vol. 7, pp. 9872-9880, 2019, doi: 10.1109/ACCESS.2018.2890127. [DOI:10.1109/ACCESS.2018.2890127]
21. [21] W. Wang, Y. Li, T. Zou, X. Wang, J. You, and Y. Luo, "A Novel Image Classification Approach via Dense-MobileNet Models," Mobile Information Systems, vol. 2020, p. 7602384, 2020/01/06 2020, doi: 10.1155/2020/7602384. [DOI:10.1155/2020/7602384]
22. [22] F. Ucar and D. Korkmaz, "COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images," Medical Hypotheses, vol. 140, p. 109761, 2020/07/01/ 2020, doi: https://doi.org/10.1016/j.mehy.2020.109761 [DOI:10.1016/j.mehy.2020.109761.] [PMID] []
23. [23] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the inception architecture for computer vision," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 2818-2826.
24. [24] A. Mahajan and S. Chaudhary, "Categorical Image Classification Based On Representational Deep Network (RESNET)," in 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), 12-14 June 2019 2019, pp. 327-330, doi: 10.1109/ICECA.2019.8822133. [DOI:10.1109/ICECA.2019.8822133]
25. [25] Z. Dong and S. Lin, "Research on image classification based on Capsnet," in 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 20-22 Dec. 2019 2019, vol. 1, pp. 1023-1026, doi: 10.1109/IAEAC47372.2019.8997743. [DOI:10.1109/IAEAC47372.2019.8997743] [PMID] []
26. [26] N. Ma, X. Zhang, H.-T. Zheng, and J. Sun, "Shufflenet v2: Practical guidelines for efficient cnn architecture design," in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 116-131.
27. [27] K. El Asnaoui and Y. Chawki, "Using X-ray images and deep learning for automated detection of coronavirus disease," Journal of Biomolecular Structure and Dynamics, pp. 1-12, 2020, doi: 10.1080/07391102.2020.1767212. [DOI:10.1080/07391102.2020.1767212] [PMID] []
28. [28] M. Loey, F. Smarandache, and N. E. M. Khalifa, "Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning," Symmetry, vol. 12, no. 4, p. 651, 2020. [Online]. Available: https://www.mdpi.com/2073-8994/12/4/651.
29. [29] S. Asif, W. Yi, H. Jin, Y. Tao, and S. Jinhai, Classification of COVID-19 from Chest X-ray images using Deep Convolutional Neural Networks. 2020.
30. [30] M. Rahimzadeh and A. Attar, "A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2," Informatics in Medicine Unlocked, vol. 19, p. 100360, 2020/01/01/ 2020, doi: https://doi.org/10.1016/j.imu.2020.100360 [DOI:10.1016/j.imu.2020.100360.] [PMID] []
31. [31] K. Medhi, M. Jamil, and M. I. Hussain, "Automatic Detection of COVID-19 Infection from Chest X-ray using Deep Learning," medRxiv, p. 2020.05.10.20097063, 2020, doi: 10.1101/2020.05.10.20097063. [DOI:10.1101/2020.05.10.20097063]
32. [32] T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, and U. Rajendra Acharya, "Automated detection of COVID-19 cases using deep neural networks with X-ray images," Computers in Biology and Medicine, vol. 121, p. 103792, 2020/06/01/ 2020, doi: https://doi.org/10.1016/j.compbiomed.2020.103792 [DOI:10.1016/j.compbiomed.2020.103792.] [PMID] []
33. [33] I. D. Apostolopoulos and T. A. Mpesiana, "Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks," Physical and Engineering Sciences in Medicine, vol. 43, no. 2, pp. 635-640, 2020/06/01 2020, doi: 10.1007/s13246-020-00865-4. [DOI:10.1007/s13246-020-00865-4] [PMID] []
34. [34] M. R. Karim, T. Döhmen, D. Rebholz-Schuhmann, S. Decker, M. Cochez, and O. Beyan, "DeepCOVIDExplainer: Explainable COVID-19 Predictions Based on Chest X-ray Images," ArXiv, vol. abs/2004.04582, 2020.
35. [35] T. Mahmud, M. A. Rahman, and S. A. Fattah, "CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization," Computers in Biology and Medicine, vol. 122, p. 103869, 2020/07/01/ 2020, doi: https://doi.org/10.1016/j.compbiomed.2020.103869 [DOI:10.1016/j.compbiomed.2020.103869.] [PMID] []
36. [36] B. Ghoshal and A. Tucker, "Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection," ArXiv, vol. abs/2003.10769, 2020.
37. [37] P. K. Sethy, S. K. Behera, P. K. Ratha, and P. Biswas, "Detection of coronavirus disease (COVID-19) based on deep features and support vector machine," 2020.
38. [38] Y. Yujun, L. Jianping, and Y. Yimei, "The research of the fast SVM classifier method," in 2015 12th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), 18-20 Dec. 2015 2015, pp. 121-124, doi: 10.1109/ICCWAMTIP.2015.7493959. [DOI:10.1109/ICCWAMTIP.2015.7493959]
39. [39] M. E. H. Chowdhury et al., "Can AI Help in Screening Viral and COVID-19 Pneumonia?," IEEE Access, vol. 8, pp. 132665-132676, 2020, doi: 10.1109/ACCESS.2020.3010287. [DOI:10.1109/ACCESS.2020.3010287]
40. [40] L. Wang, Z. Q. Lin, and A. Wong, "COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images," Scientific Reports, vol. 10, no. 1, p. 19549, 2020/11/11 2020, doi: 10.1038/s41598-020-76550-z. [DOI:10.1038/s41598-020-76550-z] [PMID] []
41. [41] A. I. Khan, J. L. Shah, and M. M. Bhat, "CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images," Computer Methods and Programs in Biomedicine, vol. 196, p. 105581, 2020/11/01/ 2020, doi: https://doi.org/10.1016/j.cmpb.2020.105581 [DOI:10.1016/j.cmpb.2020.105581.] [PMID] []
42. [42] A. S. Elkorany and Z. F. Elsharkawy, "COVIDetection-Net: A tailored COVID-19 detection from chest radiography images using deep learning," Optik, vol. 231, p. 166405, 2021/04/01/ 2021, doi: https://doi.org/10.1016/j.ijleo.2021.166405 [DOI:10.1016/j.ijleo.2021.166405.] [PMID] []
43. [43] R. Ribani and M. Marengoni, "A survey of transfer learning for convolutional neural networks," in 2019 32nd SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T), 2019: IEEE, pp. 47-57.
44. [44] W. Pan and I. Hawrysiewycz, "Assisting learners to dynamically adjust learning processes through software agents," International Journal of Intelligent Information Technologies (IJIIT), vol. 2, no. 2, pp. 1-15, 2006.
45. [45] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in neural information processing systems, vol. 25, pp. 1097-1105, 2012.
46. [46] S. Mallat, "Understanding deep convolutional networks," Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 374, no. 2065, p. 20150203, 2016, doi: doi:10.1098/rsta.2015.0203. [DOI:10.1098/rsta.2015.0203] [PMID] []
47. [47] Y. Ji, Q. Wang, X. Li, and J. Liu, "A survey on tensor techniques and applications in machine learning," IEEE Access, vol. 7, pp. 162950-162990, 2019.
48. [48] D. P. Kingma and J. Ba, "Adam: A method for stochastic optimization," arXiv preprint arXiv:1412.6980, 2014.
49. [49] J. Zhang, "Gradient descent based optimization algorithms for deep learning models training," arXiv preprint arXiv:1903.03614, 2019.
50. [50] S. Ruder, "An overview of gradient descent optimization algorithms," arXiv preprint arXiv:1609.04747, 2016.
51. [51] "Chest X-ray images (covid-19)." https://github.com/ieee8023/covid-chestxray-dataset (accessed.
52. [52] "Chest X-ray images (pneumonia)." https://www.kaggle.com/chest-xray-pneumonia (accessed.
53. [53] F. Chollet, Deep learning with Python. Manning New York, 2018.
54. [54] T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, and U. R. Acharya, "Automated detection of COVID-19 cases using deep neural networks with X-ray images," Computers in biology and medicine, vol. 121, p. 103792, 2020.
55. [55] L. Wang, Z. Q. Lin, and A. Wong, "Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images," Scientific Reports, vol. 10, no. 1, pp. 1-12, 2020.
56. [56] D. Singh and V. Kumar, "A Comprehensive Review of Computational Dehazing Techniques," Archives of Computational Methods in Engineering, vol. 26, no. 5, pp. 1395-1413, 2019/11/01 2019, doi: 10.1007/s11831-018-9294-z. [DOI:10.1007/s11831-018-9294-z]
57. [57] A. Gupta, D. Singh, and M. Kaur, "An efficient image encryption using non-dominated sorting genetic algorithm-III based 4-D chaotic maps," Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 3, pp. 1309-1324, 2020/03/01 2020, doi: 10.1007/s12652-019-01493-x. [DOI:10.1007/s12652-019-01493-x]
58. [58] M. Kaur, D. Singh, K. Sun, and U. Rawat, "Color image encryption using non-dominated sorting genetic algorithm with local chaotic search based 5D chaotic map," Future Generation Computer Systems, vol. 107, pp. 333-350, 2020/06/01/ 2020, doi: https://doi.org/10.1016/j.future.2020.02.029 [DOI:10.1016/j.future.2020.02.029.]
59. [59] M. Kaur and V. Kumar, "Beta chaotic map based image encryption using genetic algorithm," International Journal of Bifurcation and Chaos, vol. 28, no. 11, p. 1850132, 2018.
60. [60] H. S. Pannu, D. Singh, and A. K. Malhi, "Multi-objective particle swarm optimization-based adaptive neuro-fuzzy inference system for benzene monitoring," Neural Computing and Applications, vol. 31, no. 7, pp. 2195-2205, 2019/07/01 2019, doi: 10.1007/s00521-017-3181-7. [DOI:10.1007/s00521-017-3181-7]
61. [61] D. Singh, V. Kumar, and M. Kaur, "Image dehazing using window-based integrated means filter," Multimedia Tools and Applications, vol. 79, no. 47, pp. 34771-34793, 2020/12/01 2020, doi: 10.1007/s11042-019-08286-6. [DOI:10.1007/s11042-019-08286-6]
62. [62] D. Singh and V. Kumar, "Dehazing of outdoor images using notch based integral guided filter," Multimedia Tools and Applications, vol. 77, no. 20, pp. 27363-27386, 2018/10/01 2018, doi: 10.1007/s11042-018-5924-6. [DOI:10.1007/s11042-018-5924-6]
63. [63] D. Singh, V. Kumar, and M. Kaur, "Single image dehazing using gradient channel prior," Applied Intelligence, vol. 49, no. 12, pp. 4276-4293, 2019/12/01 2019, doi: 10.1007/s10489-019-01504-6. [DOI:10.1007/s10489-019-01504-6]
ارسال نظر درباره این مقاله : نام کاربری یا پست الکترونیک شما:
CAPTCHA
ارسال پیام به نویسنده مسئول

بازنشر اطلاعات
Creative Commons License این مقاله تحت شرایط Creative Commons Attribution-NonCommercial 4.0 International License قابل بازنشر است.

کلیه حقوق این تارنما متعلق به فصل‌نامة علمی - پژوهشی پردازش علائم و داده‌ها است.