ABSTRACT
One of the most difficult aspects of the present COVID19 pandemic is early identification and diagnosis of COVID19, as well as exact segregation of non-COVID19 individuals at low cost and the sickness is in its early stages. Despite their widespread use in diagnostic centres, diagnostic approaches based solely on radiological imaging have flaws given the disease's novelty. As a result, to evaluate radiological pictures, healthcare practitioners and computer scientists frequently use machine learning and deep learning models. Based on a search strategy, from November 2019 to July 2020, researchers scanned the three different databases of Scopus, PubMed, and Web of Science for this study. Machine learning and deep learning are well-established artificial intelligence domains for data mining, analysis, and pattern recognition. Deep learning in which data is passed through many layers and automatically learning the composition of each layer from large dataset and it enables a new way that evaluates the complete image without human guidance to discern which insights are valuable, with applications ranging from object detection to medical image. Deep learning with CNN may have a significant effect on the automatic recognition and extraction of crucial features from X-ray and CT Scan images related to Covid19 analysis. According to the results, models based on deep learning possess amazing abilities to offer a precise and systematic system for detecting and diagnosing COVID19. In the field of COVID19 radiological imaging, deep learning software decreases false positive and false negative errors in the identification and diagnosis of the disease. It is providing a once-in-a-lifetime opportunity to provide patients with quick, inexpensive, and safe diagnostic services while also reducing the epidemic's impact on nursing and medical staff.
- N. Chen, M. Zhou, X. Dong , “Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study,” 0e Lancet, vol. 395, no. 10223, pp. 507–513, 2020.Google ScholarCross Ref
- C. Huang, Y. Wang, X. Li , “Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China,” 0e Lancet, vol. 395, no. 10223, pp. 497–506, 2020.Google ScholarCross Ref
- C. M. A. De O. Lima, “Information about the new coronavirus disease (COVID-19),” Radiologia Brasileira, vol. 53, no. 2, 2020.Google Scholar
- T. Struyf, J. J. Deeks, J. Dinnes , “Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19 disease,” Cochrane Database of Systematic Reviews, vol. 7, no. 7, 2020.Google Scholar
- J. Liao, S. Fan, J. Chen , “Epidemiological and clinical characteristics of COVID-19 in adolescents and young adults,” 0e Innovation, vol. 1, no. 1, Article ID 100001, 2020.Google Scholar
- Organization, W. H., WHO Coronavirus Disease (COVID-19) Dashboard, World Health Organization, Geneva, Switzerland, 2020, http://Https://Covid19.%20Who.%20Int/.Google Scholar
- L. Wang, Z. Lin, and A. Wang, “COVID-net: a tailored deep convolutional neural network design for detection of COVID19 cases from chest X-ray images,” Scientific Reports, vol. 10, Article ID 19549, 2020.Google ScholarCross Ref
- WHO, WHO Coronavirus Disease (COVID-19) Dashboard, World Health Organization, Geneva, Switzerland, 2020.Google Scholar
- Q. Li, X. Guan, P. Wu , “Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia,” New England Journal of Medicine, vol. 382, no. 13, pp. 1199–1207, 2020.Google ScholarCross Ref
- G. Lippi and M. Plebani, “A six-sigma approach for comparing diagnostic errors in healthcare—where does laboratory medicine stand?” Annals of Translational Medicine, vol. 6, no. 10, 2018.Google ScholarCross Ref
- G. Lippi, A.-M. Simundic, and M. Plebani, “Potential preanalytical and analytical vulnerabilities in the laboratory diagnosis of coronavirus disease 2019 (COVID-19),” Clinical Chemistry and Laboratory Medicine (CCLM), vol. 58, no. 7, p. 1, 2020.Google ScholarCross Ref
- C. Sheridan, “Coronavirus and the race to distribute reliable diagnostics,” Nature Biotechnology, vol. 38, no. 4, p. 382, 2020.Google ScholarCross Ref
- O. Wolach and R. M. Stone, “Mixed-phenotype acute leukemia,” Current Opinion in Hematology, vol. 24, no. 2, pp. 139–145, 2017.Google ScholarCross Ref
- T. Ai, Z. Yang, H. Hou , “Correlation of chest CTand RTPCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases,” Radiology, vol. 296, no. 2, Article ID 200642, 2020.Google ScholarCross Ref
- B. A. Oliveira, L. C. Oliveira, E. C. Sabino, and T. S. Okay, “SARS-CoV-2 and the COVID-19 disease: a mini review on diagnostic methods,” Revista Do Instituto de Medicina Tropical de Sao Paulo, vol. 62, 2020.Google ScholarCross Ref
- M.-Y. Ng, E. Y. P. Lee, J. Yang , “Imaging profile of the COVID-19 infection: radiologic findings and literature review,” Radiology: Cardiothoracic Imaging, vol. 2, no. 1, Article ID e200034, 2020.Google ScholarCross Ref
- S. Wang, B. Kang, J. Ma , “A deep learning algorithm using CT images to screen for corona virus disease (COVID19).” Preprint, 2020.Google Scholar
- O. Gozes, M. Frid-Adar, H. Greenspan , “Rapid AI development cycle for the coronavirus (COVID-19) pandemic: initial results for automated detection & patient monitoring using deep learning CT image analysis,” 2020, https://arxiv.org/abs/2003.05037.Google Scholar
- J. Li, L. Xi, X. Wang , “Radiology indispensable for tracking COVID-19,” Diagnostic and Interventional Imaging, vol. 102, no. 2, 2020.Google Scholar
- S. Salehi, A. Abedi, S. Balakrishnan, and A. Gholamrezanezhad, “Coronavirus disease 2019 (COVID19): a systematic review of imaging findings in 919 patients,” American Journal of Roentgenology, vol. 215, no. 1, 2020.Google ScholarCross Ref
- S. Wang, Y. Zha, W. Li , “A, fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis,” European Respiratory Journal, vol. 56, no. 2, Article ID 2000775, 2020.Google ScholarCross Ref
- E. S. Amis, P. F. Butler, K. E. Applegate , “American college of radiology white paper on radiation dose in medicine,” Journal of the American College of Radiology, vol. 4, no. 5, pp. 272–284, 2007.Google ScholarCross Ref
- E. Baratella, P. Crivelli, C Marrocchio , “Severity of lung involvement on chest X-rays in SARS-coronavirus-2 infected patients as a possible tool to predict clinical progression: an observational retrospective analysis of the relationship between radiological, clinical, and laboratory data,” Jornal Brasileiro de Pneumologia: PublicacaoOficial da Sociedade Brasileira de Pneumologia e Tisilogia, vol. 46, no. 5, Article ID e20200226, 2020.Google ScholarCross Ref
- G. D. Rubin, C. J. Ryerson, L. B. Haramati , “*e role of chest imaging in patient management during the COVID-19 pandemic: a multinational consensus statement from the Fleischner society,” Chest, vol. 58, no. 1, 2020.Google Scholar
- K. Kallianos, J. Mongan, S. Antani , “How far have we come? artificial intelligence for chest radiograph interpretation,” Clinical Radiology, vol. 74, no. 5, pp. 338–345, 2019.Google ScholarCross Ref
- A. M. Tahir, M. E. H. Chowdhury, A. Khandakar , “A systematic approach to the design and characterization of a smart insole for detecting vertical ground reaction force (vGRF) in gait analysis,” Sensors, vol. 20, no. 4, p. 957, 2020.Google ScholarCross Ref
- G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, vol. 313, no. 5786, pp. 504–507, 2006.Google ScholarCross Ref
- M. T. Lu, A. Ivanov, T. Mayrhofer, A. Hosny, H. J. W. L. Aerts, and U. Hoffmann, “Deep learning to assess long-term mortality from chest radiographs,” JAMA Network Open, vol. 2, no. 7, Article ID e197416, 2019.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- A. A. Ardakani, A. R. Kanafi, U. R. Acharya, N. Khadem, and A. Mohammadi, “Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: results of 10 convolutional neural networks,” Computers in Biology and Medicine, vol. 121, 2020.Google ScholarCross Ref
- L. Brunese, F. Mercaldo, A. Reginelli, and A. Santone, “Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays,” Computer Methods and Programs in Biomedicine, vol. 196, 2020.Google ScholarCross Ref
- A. Chatterjee, M. W. Gerdes, and S. G. Martinez, “Statistical explorations and univariate timeseries analysis on COVID-19 datasets to understand the trend of disease spreading and death,” Sensors (Switzerland), vol. 20, no. 11, 2020.Google Scholar
- A. Jaiswal, N. Gianchandani, D. Singh, V. Kumar, and M. Kaur, “Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning,” Journal of Biomolecular Structure and Dynamics, vol. 38, pp. 1–8, 2020.Google Scholar
- Q. Ni, Z. Y. Sun, L. Qi , “A deep learning approach to characterize 2019 coronavirus sisease (COVID-19) pneumonia in chest CT images,” European Radiology, vol. 30, no. 12, 2020.Google Scholar
- T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, and U. Rajendra Acharya, “Automated detection of COVID19 cases using deep neural networks with X-ray images,” Computers in Biology and Medicine, vol. 121, 2020.Google ScholarCross Ref
- H. Panwar, P. K. Gupta, M. K. Siddiqui, R. MoralesMenendez, and V. Singh, “Application of deep learning for fast detection of COVID-19 in X-rays using nCOVnet,” Chaos, Solitons and Fractals, vol. 138, 2020.Google Scholar
- J. Song, H. Wang, Y. Liu , “End-to-end automatic differentiation of the coronavirus disease 2019 ( COVID-19) from viral pneumonia based on chest CT,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 47, no. 11, 2020.Google ScholarCross Ref
- S. Vaid, R. Kalantar, and M. Bhandari, “Deep learning COVID-19 detection bias: accuracy through artificial intelligence,” International Orthopaedics, vol. 44, 2020.Google ScholarCross Ref
- A. Waheed, M. Goyal, D. Gupta, A. Khanna, F. Al-Turjman, and P. R. Pinheiro, “CovidGAN: data augmentation using auxiliary classifier GAN for improved COVID-19 detection,” IEEE Access, vol. 8, pp. 91916–91923, 2020.Google ScholarCross Ref
- P. H. Yi, T. K. Kim, and C. T. Lin, “Generalizability of deep learning tuberculosis classifier to COVID-19 chest radiographs: new tricks for an old algorithm?” Journal of 0oracic Imaging, vol. 35, no. 4, 2020.Google Scholar
- I. D. Apostolopoulos, S. I. Aznaouridis, and M. A. Tzani, “Extracting possibly representative COVID-19 biomarkers from X-ray images with deep learning approach and image data related to pulmonary diseases,” Journal of Medical and Biological Engineering, vol. 40, no. 3, pp. 462–469, 2020.Google ScholarCross Ref
- C. Butt, J. Gill, D. Chun, and B. A. Babu, “Deep learning system to screen coronavirus disease 2019 pneumonia,” Applied Intelligence, vol. 50, 2020.Google Scholar
- D. Das, K. C. Santosh, and U. Pal, “Truncated inception net: COVID-19 outbreak screening using chest X-rays,” Physical and Engineering Sciences in Medicine, vol. 43, no. 3, 2020.Google Scholar
- A. M. Hasan, M. M. Al-Jawad, H. A. Jalab, H. Shaiba, R. W. Ibrahim, and 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, vol. 22, no. 5, 2020.Google Scholar
- H. Ko, H. Chung, W. S. Kang , “COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image: model development and validation,” Journal of Medical Internet Research, vol. 22, no. 6, Article ID e19569, 2020.Google ScholarCross Ref
- L. Li, L. Qin, Z. Xu , “Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT,” Radiology, vol. 296, no. 2, Article ID 200905, 2020.Google ScholarCross Ref
- 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, 2020.Google ScholarCross Ref
- X. Mei, H.-C. Lee, K. Diao , “Artificial intelligence- –enabled rapid diagnosis of patients with COVID-19,” Nature Medicine, vol. 26, no. 8, pp. 1224–1228, 2020.Google ScholarCross Ref
- 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, 2020.Google ScholarCross Ref
- 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, 2020.Google ScholarCross Ref
- 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,” International Journal of Mathematical, Engineering and Management Sciences, vol. 5, no. 4, pp. 643–651, 2020.Google ScholarCross Ref
- K. K. Singh, M. Siddhartha, and A. Singh, “Diagnosis of coronavirus disease (COVID-19) from chest X-ray images using modified XceptionNet,” Romanian Journal of Information Science and Technology, vol. 23, pp. S91–S105, 2020.Google Scholar
- M. To˘gaçar, B. Ergen, and Z. C¨omert, “COVID-19 detection using deep learning models to exploit social mimic optimization and structured chest X-ray images using fuzzy color and stacking approaches,” Computers in Biology and Medicine, vol. 121, Article ID 103805, 2020.Google ScholarCross Ref
- F. Ucar and D. Korkmaz, “COVIDiagnosis-Net: deep BayesSqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images,” Medical Hypotheses ,vol. 140, 2020.Google ScholarCross Ref
- Y.-C. Wang, H. Luo, S. Liu , “Dynamic evolution of COVID-19 on chest computed tomography: experience from Jiangsu province of China,” European Radiology, vol. 30, 2020.Google ScholarCross Ref
- X. Wu, H. Hui, M. Niu , “Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: a multicentre study,” European Journal of Radiology, vol. 128, 2020.Google ScholarCross Ref
- 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, Article ID 105581, 2020.Google ScholarCross Ref
- 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, vol. 38, 2020.Google Scholar
- S. Yang, L. Jiang, Z. Cao , “Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study,” Annals of Translational Medicine, vol. 8, no. 7, 2020.Google ScholarCross Ref
- M. A. Elaziz, K. M. Hosny, A. Salah, M. M. Darwish, S. Lu, and A. T. Sahlol, “New machine learning method for image-based diagnosis of COVID-19,” PLoS One, vol. 15, no. 6, Article ID e0235187, 2020.Google ScholarCross Ref
- N. Dey, V. Rajinikanth, S. J. Fong, M. S. Kaiser, and M. Mahmud, “Social group optimization-assisted Kapur's entropy and morphological segmentation for automated detection of COVID-19 infection from computed tomography images,” Cognitive Computation, vol. 12, no. 5, pp. 1011–1023, 2020.Google ScholarCross Ref
- F. A. Saiz and I. Barandiaran, “COVID-19 detection in chest X-ray images using a deep learning approach,” International Journal of Interactive Multimedia and Artificial Intelligence , vol. 6, p. 1. In press, 2020.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- F. Mart ´ınez, F. Mart ´ınez, and E. Jacinto, “Performance evaluation of the NASNet convolutional network in the automatic identification of COVID-19,” International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 2, p. 662, 2020.Google ScholarCross Ref
- Y. Pathak, P. K. Shukla, A. Tiwari, S. Stalin, and S. Singh, “Deep transfer learning based classification model for COVID-19 disease,” IRBM, vol. 41, 2020Google Scholar
Recommendations
-
Diagnosis of COVID-19 from Multimodal Imaging Data Using Optimized Deep Learning Techniques
AbstractCOVID-19 had a global impact, claiming many lives and disrupting healthcare systems even in many developed countries. Various mutations of the severe acute respiratory syndrome coronavirus-2, continue to be an impediment to early detection of this ...
-
An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works
AbstractThe World Health Organization (WHO) declared a pandemic in response to the coronavirus COVID-19 in 2020, which resulted in numerous deaths worldwide. Although the disease appears to have lost its impact, millions of people have been affected by ...
-
Deep learning based detection of COVID-19 from chest X-ray images
AbstractThe whole world is facing a health crisis, that is unique in its kind, due to the COVID-19 pandemic. As the coronavirus continues spreading, researchers are concerned by providing or help provide solutions to save lives and to stop the pandemic ...
Comments