COVID-19 identification in ct images based on deep learning models: a comparative approach
Keywords:
COVID-19, identification, epidemic rapidlyAbstract
People's lives could be in danger if a contagious disease spreads quickly, Corona-2019 virus disease (COVID-19) is one. The coronavirus epidemic rapidly spread over the world. The Corona virus has had a major impact on the health of populations and healthcare systems all over the world. RT-PCR (RT-Reverse transcription, PCR-polymerase chain reaction) testing can benefit from the use of computed tomography images. Most available methods use large training data, and the detection accuracy needs to be improved due to the inadequate border segment of symptom descriptions. This study proposes a robust and effective way for identifying normal and COVID-19 patients using small training data. Deep learning quickly creates accurate models. Data augmentation increases the training dataset to reduce over fitting and improve model generalisation. Using data augmentation, we evaluated Xception and VGG-19. The study showed that deep learning can detect COVID-19.
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Afshar, P.; Heidarian, S.; Naderkhani, F.; Oikonomou, A.; Plataniotis, K.N.; Mohammadi, A. Covid-caps: A capsule network-based framework for identification of covid-19 cases from X-ray images. Pattern Recognit. Lett. 2020, 138, 638–643.
Al-Waisy, A.; Mohammed, M.A.; Al-Fahdawi, S.; Maashi, M.; Garcia-Zapirain, B.; Abdulkareem, K.H.; Mostafa, S.; Kumar, N.M.; Le, D.N. Covid-deepnet: Hybrid multimodal deep learning system for improving covid-19 pneumonia detection in chest X-ray images. Comput. Mater. Contin. 2021, 67, 2409–2429.
Chatfield, K.; Simonyan, K.; Vedaldi, A.; Zisserman, A. Return of the devil in the details: Delving deep into convolutional nets.arXiv 2014, arXiv:1405.3531.
Chavez, S.; Long, B.; Koyfman, A.; Liang, S.Y. Coronavirus Disease (COVID-19): A primer for emergency physicians. Am. J. Emerg. Med. 2021, 44, 220–229.
Das, N.N.; Kumar, N.; Kaur, M.; Kumar, V.; Singh, D. Automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays. Irbm 2020, in press.
Elghamrawy, S.M.; Hassnien, A.E.; Snasel, V. Optimized Deep Learning-Inspired Model for the Diagnosis and Prediction of COVID-19. Comput. Mater. Contin. 2021, 67, 2353–2371.
Elshennawy, N.M.; Ibrahim, D.M. Deep-pneumonia framework using deep learning models based on chest X-ray images. Diagnostics 2020, 10, 649.
Ezzat, D.; Hassanien, A.E.; Ella, H.A. An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization. Appl. Soft Comput. 2020, 98, 106742.
F. Song, N. Shi, F. Shan, Z. Zhang, J. Shen, H. Lu, Y. Ling, Y. Jiang and Y. Shi, “Emerging 2019 Novel Coronavirus (2019-nCoV) Pneumonia,” Radiology, vol. 295, pp. 210-217, 2020.
G. Chassagnon, M. Vakalopoulou, N. Paragios and M.-P. Revel, “Artificial intelligence applications for thoracic imaging,” European Journal of Radiology, vol. 123, p. 108774, 2020.
Gaál, G.; Maga, B.; Lukács, A. Attention u-net based adversarial architectures for chest X-ray lung segmentation. arXiv 2020, arXiv:2003.10304.
Gouda, W., Almurafeh, M., Humayun, M., & Jhanjhi, N. Z. (2022). Detection of COVID-19 Based on Chest X-rays Using Deep Learning. Healthcare (Switzerland), 10(2). https://doi.org/10.3390/healthcare10020343
J. C. L. Rodrigues, S. S. Hare, A. Edey, A. Devaraj, J. Jacob, A. Johnstone, R. McStay, A. Nair and G. Robinson, “An update on COVID-19 for the radiologist-A British society of Thoracic Imaging statement,” Clinical Radiology, 2020.
J. Wu, J. Liu, X. Zhao, C. Liu, W. Wang, D. Wang, W. Xu, C. Zhang, J. Yu, B. Jiang and others, “Clinical characteristics of imported cases of COVID-19 in Jiangsu province a multicenter descriptive study,” Clinical Infectious Diseases, 2020.
J. Zhang, Y. Xie, Y. Li, C. Shen, and Y. Xia, “COVID-19 screening on chest X-ray images using deep learning based anomaly detection,” vol. 12338, 2020.
Jamil, M.; Hussain, I. Automatic detection of COVID-19 infection from chest X-ray using deep learning. medRxiv 2020.
Joaquin, A. Using Deep Learning to Detect Pneumonia Caused by NCOV-19 from X-ray Images. 2020 Available online: https://towardsdatascience.com/using-deep-learning-to-detect-ncov-19-from-x-ray-images-1a89701d1acd (accessed on 13 June 2022).
K. Roosa, Y. Lee, R. Luo et al., “Real-time forecasts of the COVID-19 epidemic in China from february 5th to February 24th, 2020,” Infectious Disease Modelling, vol. 5, pp. 256–263, 2020.
Khalil, M.I.; Tehsin, S.; Humayun, M.; Jhanjhi, N.; Zain, M.A. Multi-Scale Network for Thoracic Organs Segmentation. Comput. Mater. Contin. 2022, 70, 3251–3265.
L. Yan, H.-T. Zhang, J. Goncalves et al., “A machine learningbased model for survival prediction in patients with severe COVID-19 infection,” medRxiv, pp. 1–18, 2020.
Litjens, G.; Kooi, T.; Bejnordi, B.E.; Setio, A.A.A.; Ciompi, F.; Ghafoorian, M.; Van Der Laak, J.A.; Van Ginneken, B.; Sánchez, C.I. A survey on deep learning in medical image analysis. Med. Image Anal. 2017, 42, 60–88.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: inverted residuals and linear bottlenecks,” 2018, https://arxiv.org/abs/1801.04381.
Mair, M. D., Hussain, M., Siddiqui, S., Das, S., Baker, A., Conboy, P., Valsamakis, T., Uddin, J., & Rea, P. (2021). A systematic review and meta-analysis comparing the diagnostic accuracy of initial RT-PCR and CT scan in suspected COVID-19 patients. The British journal of radiology, 94(1119), 20201039. https://doi.org/10.1259/bjr.20201039
Mostafavi, Sayyed Mostafa, 2021, "COVID19-CT-Dataset: An Open-Access Chest CT Image Repository of 1000+ Patients with Confirmed COVID-19 Diagnosis", https://doi.org/10.7910/DVN/6ACUZJ, Harvard Dataverse, V1
Narin, A.; Kaya, C.; Pamuk, Z. Automatic detection of coronavirus disease (Covid-19) using X-ray images and deep convolutional neural networks. Pattern Anal. Appl. 2021, 24, 1207–1220.
Qingyu Chen, Alexis Allot, Zhiyong Lu, LitCovid: an open database of COVID-19 literature, Nucleic Acids Research, Volume 49, Issue D1, 8 January 2021, Pages D1534–D1540, https://doi.org/10.1093/nar/gkaa952
Rajaraman, S.; Antani, S. Training deep learning algorithms with weakly labeled pneumonia chest X-ray data for COVID-19 detection. medRxiv 2020. 10.1101/2020.05.04.20090803.
S. Chauvie, A. De Maggi, I. Baralis, F. Dalmasso, P. Berchialla, R. Priotto, P. Violino, F. Mazza, G. Melloni and M. Grosso, “Artificial intelligence and radiomics enhance the positive predictive value of digital chest tomosynthesis for lung cancer detection within SOS clinical trial,” European Radiology, p. 1–7, 2020.
S. Wang, B. Kang, J. Ma et al., “A deep learning algorithm using CT images to screen for corona virus disease (COVID- 19),” medRxiv, pp. 1–26, 2020.
Sahinbas, K.; Catak, F.O. Transfer learning-based convolutional neural network for COVID-19 detection with X-ray images. In Data Science for COVID-19; Elsevier: Amsterdam, The Netherlands, 2021; pp. 451–466.
Saiz, F., & Barandiaran, I. (2020). COVID-19 Detection in Chest X-ray Images using a Deep Learning Approach. International Journal of Interactive Multimedia and Artificial Intelligence, 6(2). https://doi.org/10.9781/ijimai.2020.04.003
Shen, D.; Wu, G.; Suk, H.I. Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 2017, 19, 221–248.
Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv 2014, arXiv:1409.1556.
Suryasa, I. W., Rodríguez-Gámez, M., & Koldoris, T. (2021). Get vaccinated when it is your turn and follow the local guidelines. International Journal of Health Sciences, 5(3), x-xv. https://doi.org/10.53730/ijhs.v5n3.2938
Toraman, S.; Alakus, T.B.; Turkoglu, I. Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks. Chaos Solitons Fractals 2020, 140, 110122.
WHO Coronavirus (COVID-19) Dashboard. Available online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019
X. Zhang, X. Zhou, M. Lin, and J. S. Shufflenet, “An extremely efficient convolutional neural network for mobile devices,” 2017, https://arxiv.org/abs/1707.01083
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