DETECTION OF COVID-19 PNEUMONIA EFFECTS IN CHEST X-RAYS USING DEEP LEARNING

Year 2022, Volume: 4 Issue: 1, 25 - 33, 26.04.2022

Mohammed Khamees Ahmed

https://izlik.org/JA28SB39HC

Abstract

The development of technological tools based on artificial intelligence (AI) could contribute significantly in the fight against COVID-19. AI is the ability of a machine to apply human cognitive functions. In this paper we propose a deep learning based model for COVID-19 detection relying on the effects it yields on the lungs.

Keywords

MRI , XRAYS , COVID-19 , Deep learning , svm

References

• Afshar, P., S. Heidarian, F. Naderkhani, A. Oikonomou, K.N. Plataniotis, and A. Mohammadi. 2020. Covid- caps: a capsule network-based framework for identification of covid-19 cases from x-ray images. arXiv preprint arXiv:2004.02696
• Ajlan, A.M., R.A. Ahyad, L.G. Jamjoom, A. Alharthy, and T.A. Madani. 2014. Middle East respiratory syndrome coronavirus (MERSCoV) infection: chest CT findings. Am J Roentgenol 203(4):782– 787
• Apostolopoulos, I.D., and T.A. Mpesiana. 2020. Covid-19: automatic detection from x-ray images utilising transfer learning with convolutional neural networks. Phys Eng Sci Med:1
• Basile, C., C. Combe, F. Pizzarelli, A. Covic, A. Davenport, M. Kanbay, D. Kirmizis, D. Schneditz, F. van der Sande, and S. Mitra. 2020. Recommendations for the prevention, mitigation and containment of the emerging SARS-CoV-2 (COVID-19) pandemic in haemodialysis centres. Nephrol Dialysis Transplantation 35:737– 741
• Chen, S.-F., Y.-C. Chen, C.-K. Yeh, F. Wang, and C. Yu. 2017. Order-free rnn with visual attention for multi- label classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
• Diamond, S., V. Sitzmann, S. Boyd, G. Wetzstein, and F. Heide. 2017. Dirty Pixels: Optimizing Image Classification Architectures for Raw Sensor Data. arXiv:1701.06487
• Dodge, S., and L. Karam. 2016. Understanding how image quality affects deep neural networks. In: Quality of Multimedia Experience (QoMEX), Eighth International Conference on, IEEE, arXiv:1604.04004v2, pp. 1–6
• Hansell, D.M., A.A. Bankier, H. MacMahon, T.C. McLoud, N.L. Muller, and J. Remy. 2008. Fleischner society: glossary of terms for thoracic imaging. Radiology 246(3):697–722
• Huang, C., Y. Wang, X. Li, L. Ren, J. Zhao, Y. Hu, L. Zhang, G. Fan, J. Xu, X. Gu, and Z. Cheng. 2020. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395(10223):497– 506
• Kanne, J.P. 2020. Chest CT findings in 2019 novel coronavirus (2019-nCoV) infections from Wuhan, China: key points for the radiologist
• Koo, H.J., S. Lim, J. Choe, S.H. Choi, H. Sung, and K.H. Do. 2018. Radiographic and CT features of viral pneumonia. Radiographics 38(3):719–739 7.
• Li, L., L. Qin, Z. Xu, Y. Yin, X. Wang, B. Kong, J. Bai, Y. Lu, Z. Fang, Q. Song, and K. Cao. 2020. Artificial intelligence distinguishes covid-19 from community acquired pneumonia on chest ct. Radiology: 200905
• Narin, A., C. Kaya, and Z. Pamuk. 2020. Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. arXiv preprint arXiv:2003.10849
• Ooi, G.C., P.L. Khong, N.L. Müller, W.C. Yiu, L.J. Zhou, J.C. Ho, B. Lam, S. Nicolaou, and K.W. Tsang. 2004. Severe acute respiratory syndrome: temporal lung changes at thin-section CT in 30 patients. Radiology 230(3):836–844
• Roosa, K., Y. Lee, R. Luo, A. Kirpich, R. Rothenberg, J.M. Hyman, P. Yan, and G. Chowell. 2020. Realtime forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020. Infect Dis Modell 5:256–263
• Wang, Y., M. Hu, Q. Li, X.P. Zhang, G. Zhai, and N. Yao. 2020. Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner. arXiv preprint arXiv:2002.05534
• Wong, K.T., G.E. Antonio, D.S. Hui, N. Lee, E.H. Yuen, A. Wu, C.B. Leung, T.H. Rainer, P. Cameron, S.S. Chung, and J.J. Sung. 2003. Severe acute respiratory syndrome: radiographic appearances and pattern of progression in 138 patients. Radiology 228(2):401–406
• Xie, X., X. Li, S. Wan, and Y. Gong. 2006. Mining x-ray images of SARS patients. In: Data Mining. Springer, Berlin, pp 282–294
• Xu, B., X.A. Meng. 2020. Deep learning algorithm using CT images to screen for Corona Virus Disease (COVID- 19)
• Yan, L., H.T. Zhang, Y. Xiao, M. Wang, C. Sun, J. Liang, S. Li, M. Zhang, Y. Guo, Y. Xiao, and X. Tang. 2020. Prediction of criticality in patients with severe Covid-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan. medRxiv
• Yao, L., E. Poblenz, D. Dagunts, B. Covington, D. Bernard, and K. Lyman. 2017. Learning to diagnose from scratch by exploiting dependencies among labels. arXiv preprint arXiv:1710.10501