Extended VGG16 Deep-Learning Detects COVID-19 from Chest CT Images

Document Type : Research Article

Authors

1 CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran

2 Department of Anatomical Sciences, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran

3 CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran- Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, MI, USA

Abstract

Coronavirus disease 2019 (COVID-19), is a rapidly spreading disease that has infected millions of people worldwide. One of the essential steps to prevent spreading COVID-19 is an effective screening of infected individuals. In addition to clinical tests like Reverse Transcription-Polymerase Chain Reaction (RT-PCR), medical imaging techniques such as Computed Tomography (CT) can be used as a rapid technique to detect and evaluate patients infected by COVID-19. Conventionally, CT-based COVID-19 detection is performed by an expert radiologist. In this paper, we will completely and utterly discuss COVID-19. We present a deep learning Convolutional Neural Network (CNN) model that we have developed to detect chest CT images with COVID-19 lesions. Afterwards, based on the fact that in an infected individual, more than one slice is involved, we determine and apply the best threshold to detect COVID-19 positive patients. We collected 5,225 CT images from 130 COVID-19 positive patients and 4,955 CT images from 130 healthy subjects. We used 3,684 CT images with COVID-19 lesions and their corresponding slices from healthy control subjects to build our model. We used 5-fold-cross-validation to evaluate the model, in which each fold contains 26 patients and 26 healthy subjects. We obtained a sensitivity of 91.5%±6.8%, a specificity of 94.6%±3.4%, an accuracy of 93.0%±3.9%, a precision of 94.5%±3.5%, and an F1-Score of 0.93±0.04.

Keywords

Main Subjects

References

[1] S. Hu, Y. Gao, Z. Niu, Y. Jiang, L. Li, X. Xiao, M. Wang, E.F. Fang, W. Menpes-Smith, J. Xia, Weakly supervised deep learning for covid-19 infection detection and classification from ct images, IEEE Access, 8 (2020) 118869-118883.
[2] L. Wang, Z.Q. Lin, A. Wong, Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images, Scientific Reports, 10(1) (2020) 1-12.
[3] W. Wang, Y. Xu, R. Gao, R. Lu, K. Han, G. Wu, W. Tan, Detection of SARS-CoV-2 in different types of clinical specimens, Jama, 323(18) (2020) 1843-1844.
[4] P. Silva, E. Luz, G. Silva, G. Moreira, R. Silva, D. Lucio, D. Menotti, COVID-19 detection in CT images with deep learning: A voting-based scheme and cross-datasets analysis, Informatics in Medicine Unlocked, 20 (2020) 100427.
[5] D. Singh, V. Kumar, M. Kaur, Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks, European Journal of Clinical Microbiology & Infectious Diseases, 39(7) (2020) 1379-1389.
[6] J. Chen, L. Wu, J. Zhang, L. Zhang, D. Gong, Y. Zhao, Q. Chen, S. Huang, M. Yang, X. Yang, Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography, Scientific reports, 10(1) (2020) 1-11.
[7] P. Angelov, E. Soares, Explainable-by-design approach for covid-19 classification via ct-scan, medRxiv,  (2020).
[8] S.S. Paima, N. Hasanzadeh, A. Jodeiri, H. Soltanian-Zadeh, Detection of COVID-19 from Chest Radiographs: Comparison of Four End-to-End Trained Deep Learning Models, in:  2020 27th National and 5th International Iranian Conference on Biomedical Engineering (ICBME), IEEE, 2020, pp. 217-221.
[9] S.B. Desai, A. Pareek, M.P. Lungren, Deep learning and its role in COVID-19 medical imaging, Intelligence-based medicine, 3 (2020) 100013.
[10] F. Shi, J. Wang, J. Shi, Z. Wu, Q. Wang, Z. Tang, K. He, Y. Shi, D. Shen, Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for covid-19, IEEE reviews in biomedical engineering,  (2020).
[11] N. Yang, F. Liu, C. Li, W. Xiao, S. Xie, S. Yuan, W. Zuo, X. Ma, G. Jiang, Diagnostic classification of coronavirus disease 2019 (COVID-19) and other pneumonias using radiomics features in CT chest images,  (2020).
[12] M. Barstugan, U. Ozkaya, S. Ozturk, Coronavirus (covid-19) classification using ct images by machine learning methods, arXiv preprint arXiv:2003.09424,  (2020).
[13] C. Zheng, X. Deng, Q. Fu, Q. Zhou, J. Feng, H. Ma, W. Liu, X. Wang, Deep learning-based detection for COVID-19 from chest CT using weak label, MedRxiv,  (2020).
[14] X. Wang, X. Deng, Q. Fu, Q. Zhou, J. Feng, H. Ma, W. Liu, C. Zheng, A weakly-supervised framework for COVID-19 classification and lesion localization from chest CT, IEEE transactions on medical imaging, 39(8) (2020) 2615-2625.
[15] A. Amyar, R. Modzelewski, H. Li, S. Ruan, Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation, Computers in Biology and Medicine, 126 (2020) 104037.
[16] K. He, W. Zhao, X. Xie, W. Ji, M. Liu, Z. Tang, Y. Shi, F. Shi, Y. Gao, J. Liu, Synergistic learning of lung lobe segmentation and hierarchical multi-instance classification for automated severity assessment of COVID-19 in CT images, Pattern recognition,  (2021) 107828.
[17] Z. Yu, X. Li, H. Sun, J. Wang, T. Zhao, H. Chen, Y. Ma, S. Zhu, Z. Xie, Rapid identification of COVID-19 severity in CT scans through classification of deep features, BioMedical Engineering OnLine, 19(1) (2020) 1-13.
[18] X. Deng, H. Shao, L. Shi, X. Wang, T. Xie, A classification–detection approach of COVID-19 based on chest X-ray and CT by using keras pre-trained deep learning models, Computer Modeling in Engineering & Sciences, 125(2) (2020) 579-596.
[19] M. Loey, G. Manogaran, N.E.M. Khalifa, A deep transfer learning model with classical data augmentation and cgan to detect covid-19 from chest ct radiography digital images, Neural Computing and Applications,  (2020) 1-13.
[20] X. He, S. Wang, S. Shi, X. Chu, J. Tang, X. Liu, C. Yan, J. Zhang, G. Ding, Benchmarking deep learning models and automated model design for covid-19 detection with chest ct scans, medRxiv,  (2020).
[21] M.Y. Kamil, A deep learning framework to detect Covid-19 disease via chest X-ray and CT scan images, International Journal of Electrical & Computer Engineering (2088-8708), 11(1) (2021).
[22] A. Shalbaf, M. Vafaeezadeh, Automated detection of COVID-19 using ensemble of transfer learning with deep convolutional neural network based on CT scans, International journal of computer assisted radiology and surgery,  (2020) 1-9.
[23] A.M. Hasan, M.M. Al-Jawad, H.A. Jalab, H. Shaiba, R.W. Ibrahim, A.a.R. AL-Shamasneh, Classification of covid-19 coronavirus, pneumonia and healthy lungs in ct scans using q-deformed entropy and deep learning features, Entropy, 22(5) (2020) 517.
[24] L. Sun, Z. Mo, F. Yan, L. Xia, F. Shan, Z. Ding, B. Song, W. Gao, W. Shao, F. Shi, Adaptive feature selection guided deep forest for covid-19 classification with chest ct, IEEE Journal of Biomedical and Health Informatics, 24(10) (2020) 2798-2805.
[25] A.K. Abdar, S.M. Sadjadi, H. Soltanian-Zadeh, A. Bashirgonbadi, M. Naghibi, Automatic Detection of Coronavirus (COVID-19) from Chest CT Images using VGG16-Based Deep-Learning, in:  2020 27th National and 5th International Iranian Conference on Biomedical Engineering (ICBME), IEEE, 2020, pp. 212-216.
[26] N. Hasanzadeh, S.S. Paima, A. Bashirgonbadi, M. Naghibi, H. Soltanian-Zadeh, Segmentation of COVID-19 Infections on CT: Comparison of Four UNet-Based Networks, in:  2020 27th National and 5th International Iranian Conference on Biomedical Engineering (ICBME), IEEE, 2020, pp. 222-225.
[27] A. Sarkar, J. Vandenhirtz, J. Nagy, D. Bacsa, M. Riley, Identification of images of COVID-19 from Chest Computed Tomography (CT) images using Deep learning: Comparing COGNEX VisionPro Deep Learning 1.0 Software with Open Source Convolutional Neural Networks,  (2020).
[28] H. Alshazly, C. Linse, E. Barth, T. Martinetz, Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning, Sensors, 21(2) (2021) 455.
[29] G. Huang, Z. Liu, L. Van Der Maaten, K.Q. Weinberger, Densely connected convolutional networks, in:  Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 4700-4708.
[30] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, 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.
[31] 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.
[32] S. Liu, W. Deng, Very deep convolutional neural network based image classification using small training sample size, in:  2015 3rd IAPR Asian conference on pattern recognition (ACPR), IEEE, 2015, pp. 730-734.
[33] K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in:  Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.
[34] N. Ketkar, E. Santana, Deep learning with Python, Springer, 2017, page:172.
[35] A. Amyar, S. Ruan, I. Gardin, R. Herault, C. Clement, P. Decazes, R. Modzelewski, Radiomics-net: Convolutional neural networks on FDG PET images for predicting cancer treatment response, Journal of Nuclear Medicine, 59(supplement 1) (2018) 324-324.
AUT Journal of Electrical Engineering
Volume 54, Issue 1
June 2022
Pages 79-90

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