Detection of COVID-19 Based on Chest Medical Imaging and Artificial Intelligence Techniques
Engineering and Technology Journal,
2021, Volume 39, Issue 10, Pages 1588-1600
AbstractThe emergence of COVID-19 disease in the world has moved the wheel of scientific research in order to detect it in the best method, and the fastest of these methods is the use of Artificial Intelligence (AI) techniques to help medical professionals detect COVID-19. The proposed topic is aim to develop algorithm based on combination between imageprocessing techniques with artificial intelligence to diagnose COVID-19. The proposed algorithm consists of five stages to detect and classify COVID-19 from Computer Tomography (CT) images. These stages include; The first of these stages is to collect data from hospitals as real data and from Kagglewebsite for patients and healthy people, then the stage before removing the noise and converting it from RGB to grayscale, then we improve the image, segmentation and formalities, the other stage is a stage used to extract the important characteristics, and the last stage is the classification of images CT scan using Feed Forward Back Propagation Network (FFBPN) and Support Vector Machine (SVM )and compare the result between them and see if the person is infected or healthy. This study was implemented in MATLAB software. The results showed that the noise cancellation technology using anisotropic filtering gave the best results. As for the optimization technology, only the brightness of the images has been increased. At the stage of segmentation of the area of lung injection using the area transplant method, the best results are detection of COVID-19 from other healthy tissues. The FFBPN gave the best results for detecting and classifying COVID-19 as well as determining whether a person has been infected or not. The results of the proposed methodology in accurate and rapid detection of COVID-19 in the lung. The contribution of this paper is to help medical staff detect COVID-19 without human intervention.
- Detection of COVID-19 was studied.
- Classification of images CT scan using Feed Forward Back Propagation Network (FFBPN) and Support Vector Machine (SVM) was implemented.
- SVM gives 96% accuracy and FFBPN gives 98.5% accuracy.
 R. Arya K.K., M.Kausar, D.Bisht, D.Kumar, D.Sati, G.Rajpal , Recent Diagnostic Techniques for COVID-19. In: Kautish S., Peng SL., Obaid A.J. (eds) Computational Intelligence Techniques for Combating COVID-19. EAI/Springer Innovations in Communication and Computing. Springer, Cham., (2020).
 C. Huang, Y. Wang, Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China Lancet., (2020).
 World Health Organization, Pneumonia of Unknown Cause–China. Emergencies Preparedness, Response, Disease Outbreak News, World Health Organization (WHO), (2020).
 Z. Wu, J.M. McGoogan, ,Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention Jama 323, (2020).
 M.L. Holshue, C. DeBolt, First case of 2019 novel coronavirus in the United States, N. Engl. J. Med., (2020).
 W. Kong, P.P. Agarwal, Chest imaging appearance of COVID-19 infection, Radiology: Cardiothoracic Imaging 2, (2020).
 T. Singhal. A Review of Coronavirus Disease-2019 (COVID-19) Indian journal of pediatrics, 87(4), 281–286. https://doi.org/10.1007/s12098-020-03263-6, (2020).
 United imaging's emergency radiology departments support mobile cabin hospitals, facilitate 5G remote diagnosis. Available: https://www.prnewswire.com/news-releases/united-imagings-emergency-radiology-departments-support-mobile-cabin-hospitals-facili tate-5g-remote-diagnosis-301010528.html.
 Y. Wang, Lu X, Zhang Y, Zhang X, Wang K, Liu J, Li X, Hu R, Meng X, Dou S, Hao H, Zhao X, Hu W, Li C, Gao Y, Wang Z, Lu G, Yan F, Zhang B. Precise pulmonary scanning and reducing medical radiation exposure by developing a clinically applicable intelligent CT system: Toward improving patient care. EBioMedicine, (2020).
 R. Li, C. Cai, G. Georgakis, S. Karanam, T. Chen, and Z. Wu, Towards robust RGB-D human mesh recovery, arXiv:1911.07383, (2019).
 J.-H. Lee, D.-i. Kim, and M.-k. Cho, Computed tomography apparatus and method of controlling X-ray by using the same, ed: Google Patents, (2017).
 P. Forthmann and G. Pfleiderer, Augmented display device for use in a medical imaging laboratory, ed: Google Patents, (2019).
 V. T. Jensen, "Method and system of acquiring images with a medical imaging device," ed: Google Patents. (2009).
 Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh, Realtime multi-person 2d pose estimation using part affinity fields, in Proceedings of theIEEE Conference on Computer Vision and Pattern Recognition, (2017), 7291-7299.
 United imaging sends out more than 100 CT scanners and X-ray machines to aid diagnosis of the coronavirus. Available: https://www.itnonline.com/content/united-imaging-sends-out-more-100-ct-scanners-and-x-ray-machines-aid-diagnosis-coronavirus.
 United imaging aids fight against coronavirus, (2020), Available:https://www.auntminnie.com/index.aspx?sec=log&itemID=128062.
 CIMC delivers mobile CT scan cabin to Huangzhou General Hospital to diagnose coronavirus. Available: https://www.hhmglobal.com/industry-updates/press-releases/cimcdelivers- mobile-ct-scan-cabin-to-huangzhou-general-hospital-to-di
 agnose-coronavirus Prehospital CT scans possible with mobile stroke unit Available: https://www.ems1.com/ems-products/ambulances/articles/prehospi tal-ct-scans-possible-with-mobile-stroke-unit-4JKu37U2neG4k68j/
 S. Khobragade, A. Tiwari, C.Y. Pati1 and V. Narke, (2016) Automatic Detection of Major Lung Diseases Using Chest Radiographs and Classification by Feed-forward Artifieial Neural Network 1st IEEE International Conference on Power Electronics. Intelligent Control and Energy Systems (ICPEICES).
 A. Mathur , Detecting Myocardial Infarctions Using Machine Learning Methods, M.Sc. Thesis, San Jose State University, (2019).
 M. Vázquez Enríquez, A Deep Learning Approach for Pneumonia Detection on Chest X-Ray, M.Sc Thesis Telecommunications Engineering School, (2019).
 B. Lakshmipriya, K. Jayanthi, B. Pottakkat and G. Ramkumar, Liver Segmentation using Bidirectional Region Growing with Edge Enhancement in NSCT Domain," IEEE International Conference on System, Computation, Automation and Networking (ICSCA), Pondicherry, India, (2018) 1-5, doi: 10.1109/ICSCAN.2018.8541257.
 G., Megha, Morphological image processing." IJCST, 2, (2011).
 D.Graupe Principle of Artificial Neural Networks. Singapore: World Scientific, (2007).
 S., Khushboo; V., Satya. Detecting Brain Mri Anomalies By Using Svm Classification. International Journal of Engineering Research and Applications (IJERA), 2, (2012), 724-726.
 M.S. Uzer, N. Yilmaz and O. Inan, Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines F=for Medical Datasets Classification. The Scientific World Journal, (2013).
 F. Melgani And L. Bruzzone, Classification of Hyper spectral Remote Sensing Images With Support Vector Machines, IEEE Transactions on Geoscience and Remote Sensing, 42, (2004), Issue 8, 1778-1790.
 N. Zayed, H. A. Elnemr, Statistical Analysis of Haralick Texture Features to Discriminate Lung Abnormalities, International Journal of Biomedical Imaging, (2015).
- Article View: 118
- PDF Download: 70