Detection of Covid-19 from Chest CT Images using Xception Architecture: A Deep Transfer Learning based Approach

Year 2021, Volume: 25 Issue: 3 , 800 - 810 , 30.06.2021

Özlem Polat

https://doi.org/10.16984/saufenbilder.903886

Cited By: 13

https://izlik.org/JA96GZ59ZP

Abstract

Covid-19 infection, which first appeared in Wuhan, China in December 2019, affected the whole world in a short time like three months. The disease caused by the virus called SARS-CoV-2 affects many organs, especially the lungs, brain, liver and kidney, and causes a large number of deaths. Early detection of Covid-19 using computer-aided methods will ensure that the patient reaches the right treatment without wasting time, and the spread of the disease will be controlled. This study proposes a solution for detecting Covid-19 using chest computed tomography (CT) scan images. Firstly, image features are extracted using Xception network, convolutional neural network (CNN) based transfer learning architecture, then classification process is performed with a fully connected neural network (FCNN) added at the end of this architecture. The classification model was tested ten times on the publicly available SARS-CoV-2-CT-scan dataset containing 2482 CT images labelled as covid and non-covid. The precision, recall, f1-score and accuracy metrics were used as performance measures. While obtaining an average of 98.89% accuracy, in the best case, 99.59% classification performance was achieved. Xception outperforms other methods in the literature. The results promise that the proposed method can be evaluated as a clinical option helping experts in the detection of Covid-19 from CT images.

Keywords

Covid-19 , Classification , Deep learning , Xception

References

• [1] S.E. Park, “Epidemiology, virology, and clinical features of severe acute respiratory syndrome - coronavirus-2 (SARS-CoV-2; Coronavirus Disease-19)”, Clin Exp Pediatr vol. 63, no. 4, pp. 119-124, 2020. doi:10.3345/cep.2020.00493
• [2] C.C. Lai, C.Y. Wang, Y.H. Wang, S.C. Hsueh, W.C. Ko et al., “Global epidemiology of coronavirus disease 2019 (COVID19): disease incidence, daily cumulative index, mortality, and their association with country healthcare resources and economic status” Int J Antimicrob Agents, vol. 55, no. 4, pp. 105946, 2020. doi:10.1016/j.ijantimicag.2020.105946
• [3] WHO. Coronavirus disease (covid-2019) r&d. https://www.who.int/blueprint/ priority-diseases/key-action/novel-coronavirus/en/ Last access date 02.04.2021.
• [4] V.M. Corman, O. Landt, M. Kaiser, R. Molenkamp, A. Meijer et al., “Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR”, Euro Surveill, vol. 25, no. 3, pp. 2000045, 2020. doi:10.2807/1560-7917.ES.2020.25.3.2000045
• [5] Y. Yang, M. Yang, C. Shen, F. Wang, J. Yuan et al., “Evaluating the accuracy of different respiratory specimens in the laboratory diagnosis and monitoring the viral shedding of 2019-nCoV infections”, medRxiv, 2020. doi:10.1101/2020.02.11.200214
• [6] M. Chung, A. Bernheim, X. Mei, N. Zhang, M. Huang et al., “CT imaging features of 2019 novel coronavirus (2019-nCoV)”, Radiology, vol. 295, no. 1, pp. 202-207, 2020. doi:10.1148/radiol.2020200230
• [7] T. Ai, Z. Yang, H. Hou, C. Zhan, C. Chen et al., “Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: A report of 1014 cases”, Radiology, vol. 296, no. 2, pp. E32-E40, 2020. doi:10.1148/radiol.2020200642
• [8] G. Litjens, T. Kooi, B.E. Bejnordi, A.A.A. Setio, F. Ciompi et al., “A survey on deep learning in medical image analysis”, Medical Image Analysis, vol. 42, pp. 60-88, 2017. doi:10.1016/j.media.2017.07.005
• [9] H. Panwar, P.K. Gupta, M.K. Siddiqui, R. Morales-Mendenez, V. Singh, “Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet”, Chaos, solitons and fractals, vol. 138, pp. 109944, 2020. doi:10.1016/j.chaos.2020.109944
• [10] I.D. Apostolopoulos, T.A. Mpesiana, “Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks”, Phys Eng Sci Med, vol. 43, pp. 635-640, 2020. doi:10.1007/s13246-020-00865-4
• [11] L. Li, L. Qin, Z. Xu, Y. Yin, X. Wang et al., “Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: Evaluation of the diagnostic accuracy”, Radiology, vol. 269, no. 2, pp. E65-E72, 2020. doi:10.1148/radiol.2020200905
• [12] G. Jain, D. Mittal, D. Thakur, M.K. Mittal, “A deep learning approach to detect Covid-19 coronavirus with X-ray images”, Biocybernetics and Biomedical Engineering, vol. 40, no. 1, pp. 1391-1405, 2020. doi:10.1016/j.bbe.2020.08.008
• [13] S. A. Harmon, T.H. Sanford, S. Xu, E.B. Turkbey, H. Roth et al., “Artificial intelligence for the detetion of Covid-19 pneumonia on chest CT using multinational datasets”, Nat Commun, vol. 11, pp. 4080, 2020. doi:10.1038/s41467-020-17971-2
• [14] M. Rahimzadeh and A. Attar, “A modified deep convolutional neural network for detecting Covid-19 and pneumonia from chest x-ray images based on concatenation of Xception and ResNet50V2”, Informatics in Medicine Unlocked, vol. 19, pp. 100360, 2020. doi:10.1016/j.imu.2020.100360
• [15] T. Ozturk, M. Talo, E.A. Yildirim, U.B. Baloglu, O. Yildirim et al., “Automated detection of Covid-19 cases using deep neural networks with X-ray images”, Computers in Biology and Medicine, vol. 121, pp. 103792, 2020. doi:10.1016/j.compbiomed.2020.103792.
• [16] L. Wang, Z.Q. Lin, A. Wong, “COVID-Net: A tailored deep convolutional neural network design for detection of Covid-19 cases from chest radiography images”, arXiv, arXiv:2003.09781, 2020. https://arxiv.org/abs/2003.09871
• [17] P.K. Sethy, S.K. Behera, P.K. Ratha, P. Biswas, “Detection of coronavirus disease (Covid-19) based on deep features ansd support vector machine”, International Journal of Mathematical, Engineering and Management Sciences, vol. 4, no. 5, pp. 642-651, 2020. doi:10.33889/IJMEMS.2020.5.4.052
• [18] E.E.D. Hemdan, M.A. Shouman, M.E. Karar, “COVIDX-Net: A framework of deep learning classifiers to diagnose Covid-19 in X-ray images”, arXiv, arXiv:2003.11055, 2020. https://arxiv.org/abs/2003.11055
• [19] A. Narin, C. Kaya, Z. Pamuk, “Automatic detection of coronavirus disease (Covid-19) using x-ray images and deep convolutional neural networks”, arXiv, arXiv:2003.10849, 2020. https://arxiv.org/abs/2003.10849
• [20] S. Ying, S. Zheng, L. Li, X. Zhang, X. Zhang et al., “Deep learning enables accurate diagnosis of novel coronavirus (Covid 19) with CT images”, medRxiv, 2020. doi:10.1101/2020.02.23.20026930
• [21] S. Wang, B. Kang, J. Ma, X. Zeng, M. Xiao et al., “A deep learning algorithm using CT images to secreen for coronavirus disease (Covid-19)”, medRxiv, 2020. doi:10.1101/2020.02.14.20023028
• [22] X. Wang, X. Deng, Q. Fu, Q. Zhou, J. Feng et al., “A weakly-supervised framework for Covid-19 classification and lesion localization from chest CT”, IEEE Trans Med Imaging, vol. 39, no. 8, pp. 2615-2625, 2020. doi:10.1109/TMI.2020.2995965
• [23] X. Xu, X. Jiang, C. Ma, P. Du, X. Li et al., “Deep learning system to screen coronavirus disease 2019 pneumonia”, arXiv, 2002.09334, 2020. https://arxiv.org/abs/2002.09334
• [24] S.H. Yoo, H. Geng, T.L. Chiu, S.K. Yu, D.C. Cho et al., “Deep learning-based decision-tree classifier for Covid-19 diagnosis from chest X-ray imaging”, Front Med, vol. 7, no. 427, pp. 1-8, 2020. doi: 10.3389/fmed.2020.00427
• [25] S. Albahli, “A deep neural network to distinguish covid-19 from other chest diseases using X-ray images”, Curr Med Imaging Rev, vol. 16, pp. 1-11, 2020. doi: 10.2174/1573405616666200604163954
• [26] J. Civit-Masot, F. Luna-Perejon, M.D. Morales, A. Civit, “Deep learning system for Covid-19 diagnosis aid using X-ray pulmonary images”, Appl Sci, vol. 10, no. 13, pp. 4060, 2020. doi:10.3390/app10134640
• [27] D. Singh, V. Kumar, K. Vaishali, M. Kaur, “Classification of Covid-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks”, Eur J Clin Microbiol Infect Dis, vol.39, pp. 1379-1389, 2020. doi: 10.1007/s10096-020-03901-z
• [28] S. Ahuja, B.K. Panigrahi, N. Dey, V. Rajinikanth, T.K. Gandhi, “Deep transfer learning-based automated detection of Covid-19 from lung CT scan slices”, Appl Intell, pp. 1-15, 2020. doi:10.1007/s10489-020-01826-w
• [29] E. Soares, P. Angelov, S. Biaso, M.H. Froes, D.K Abe, "SARS-CoV-2 CT Scan Dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification", MedRxiv, 2020.
• [30] 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 Anaşysis", Informatics in Medicine Unlocked, vol. 20, pp. 100427, 2020.
• [31] S. Yazdani, S. Minaee, R. Kafieh, N. Saeedizadeh, M. Sonka, "Covid CT-Net: Predicting Covid-19 from Chest CT Images using Attentional Convolutional Network", arXiv, 2020.
• [32] D. Konar, B.K. Panigrahi, S. Bhattacharyya, N. Dey, "Auto-Diagnosis of COVID-19 using lung CT images with semi-supervised shallow learning network”, IEEE Access, vol. 9, pp. 28716-28728, 2020. doi: 10.1109/ACCESS.2021.3058854.
• [33] Y. LeCun, B. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, L.D. Jackel, “Backpropagation applied to handwritten zip code recognition”, Neural Comput, vol. 1, no. 4, pp. 541-551, 1989. doi: 10.1162/neco.1989.1.4.541
• [34] I. Goodfellow, Y. Bengio, A. Courville, "Deep Learning", MIT Press, 2016.
• [35] V. Nair, G.E. Hinton, "Rectified Linear Units Improve Restricted Boltzmann Machines", In Proc.: 27th International Conference on Machine Learning (ICML'10), June 21-24, Haifa, Israel pp. 807-814, 2010.
• [36] Y. LeCun, L. Jackel, L. Bottou, C. Cortes, J.S. Denker, H. Drucker, I. Guyon, U. Muller, E. Sackinger, P. Simard et al., “Learning algorithms for classification: A comparison on handwritten digit recognition”, Neural Networks: The Statistical Mechanics Perspective, pp. 261-276, 1995.
• [37] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, “Going deeper with convolutions”, In: Proceedings of the IEEE conference on computer vision and pattern recognition. arXiv:1409.4842, 2014. https://arxiv.org/abs/1409.4842
• [38] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, “Rethinking the inception architecture for computer vision”, arXiv:1512.00567, 2015. https://arxiv.org/abs/1512.00567
• [39] C. Szegedy, S. Ioffe, V. Vanhoucke, A. Alemi, “Inception-v4, Inception-ResNet and the impact of residual connections on learning”, arXiv:1602.07261, 2016. https://arxiv.org/abs/1602.07261
• [40] F. Chollet, “Xception: Deep learning with depthwise separable convolutions”, arXiv, arXiv:1610.02357v3, 2017. https://arxiv.org/abs/1610.02357
• [41] L. Sifre, “Rigid-motion scattering for image classification”, Ph.D. thesis, 2014.
• [42] F. Chollet, “Keras”, 2015. https://github.com/fchollet/keras
• [43] A. Martin, A. Agarwal, P. Barham, E. Brevdo, Z. Chen et al., "TensorFlow: Large-scale machine learning on heterogeneous systems" (software available from: tensorflow.org), 2015.
• [44] M.D. Zeiler, “Adadelta: An adaptive learning rate method”, ArXiv abs/1212.5701, 2012. https://arxiv.org/abs/1212.5701v1