Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Call For Paper : Vol 11, Issue 08, August 2024

Call for Paper

Vol 11, Issue 08, August 2024

Indexing

Design and Implementation of Detection of Covid Using CT Scan Images

Author : Mr. Ishwar Kharat 1 Dr. Avinash S. Kapse 2

PDF

Date of Publication :5th June 2021

Abstract: The diagnosis of any disease is like the light at the end of the tunnel. In the case of the COVID-19 pandemic, the importance of earlier diagnosis and detecting the disease is beyond measure. The initial focus must be on the data by which we need to efficiently train a model. This data will help Machine Learning (ML). or Deep Learning (DL) algorithms to diagnose COVID-19 cases. Due to the disadvantages of RT-PCR, researchers adopted an alternative method which is the use of Artificial Intelligence on chest CT or X-Ray images to diagnose COVID-19. Fundamentally, a chest CT image is an image taken using the computed tomography (CT) scan procedure where X-Ray images are captured from different angles and compiled to form a single image. Although a CT scan consumes less time to demonstrate, it is fairly expensive. As a result, many researchers adopted X-Ray images instead of CT images to develop a COVID-19 detection model. A chest X-Ray is a procedure of using X-Rays to generate images of the chest. Also, it is relatively economical and convenient to maintain.

Reference :

    1. Ajesh F, Ravi R, G R, (2020) Early diagnosis of glaucoma using multifeature analysis and dbn based classification. J Ambient Intell Human Comput. https ://doi.org/10.107/s1265 2-020-01771 –z
    2. Bajaj N, Granwehr BP, Hanna EY, Chambers MS (2020) Salivary detection of sars-cov-2 (covid-19) and implications for oral healthcare providers. Head & Neck
    3. Bishop CM (2006) Pattern recognition and machine learning. springer, Berlin
    4. Chen F, Liu Z, Zhang F, Xiong R, Chen Y, Cheng X, Wang W, Ren J (2020a) First case of severe childhood novel coronavirus pneumonia in china. Zhonghua er ke za zhi Chinese Journal of Pediatrics 58:E005–E005
    5. Chen J, Wu L, Zhang J, Zhang L, Gong D, Zhao Y, Hu S, Wang Y, Hu X, Zheng B, et al. (2020b) Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study. medRxiv
    6. Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, Qiu Y, Wang J, Liu Y, Wei Y et al (2020c) Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. The Lancet 395(10223):507–513
    7. Dataset (2020) Covid data. https ://drive .googl e.com/uc?id=1coM7 x3378 f-Ou2l6 Pg2wl daOI7 Dntu1 a, online: accessed 22 April 2020
    8. Di Gennaro F, Pizzol D, Marotta C, Antunes M, Racalbuto V, Veronese N, Smith L (2020) Coronavirus diseases (covid-19) current status and future perspectives: a narrative review. Int J Environ Res Pub Health 17(8):2690
    9. Documentation K (2018) Imagedatagenerator class. https ://keras .io/ prepr ocess ing/image /#image datag enera tor-metho ds, online: accessed 28 April 2020
    10. Gozes O, Frid-Adar M, Greenspan H, Browning PD, Zhang H, Ji W, Bernheim A, Siegel E (2020) Rapid ai development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection & patient monitoring using deep learning ct image analysis. arXiv preprint arXiv :20030 5037
    11. Hajian-Tilaki K (2013) Receiver operating characteristic (roc) curve analysis for medical diagnostic test evaluation. Caspian J Int Med 4(2):627
    12. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
    13. He T, Zhang L, Guo J, Yi Z (2020) Multilabel classification by exploiting data-driven pair-wise label dependence. Int J Intell Syst
    14. Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR (2012) Improving neural networks by preventing coadaptation of feature detectors. arXiv preprint arXiv :12070 580 
    15. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X et al (2020a) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet 395(10223):497–506
    16. Huang JS, Chen BQ, Zeng NY, Cao XC, Li Y (2020b) Accurate classification of ecg arrhythmia using mowpt enhanced fast compression deep learning networks. J Ambient Intell Human Comput. https ://doi.org/10.1007/s1265 2-020-02110 -y
    17. Imtiaz Z, Umer M, Ahmad M, Ullah S, Choi GS, Mehmood A (2020) Duplicate questions pair detection using siamese malstm. IEEE Access 8:21932–21942
    18. Jackson D, Anders R, Padula WV, Daly J, Davidson PM (2020) Vulnerability of nurse and physicians with covid-19: Monitoring and surveillance needed. J Clin Nurs
    19. Jiménez-Valverde A (2012) Insights into the area under the receiver operating characteristic curve (auc) as a discrimination measure in species distribution modelling. Global Ecolo Biogeogr 21(4):498–507
    20. Jin S, Wang B, Xu H, Luo C, Wei L, Zhao W, Hou X, Ma W, Xu Z, Zheng Z, et al. (2020) Ai-assisted ct imaging analysis for covid- 19 screening: Building and deploying a medical ai system in four weeks. medRxiv
    21. Kaggle (2020) Covid-19 patients lungs x ray images 10000. https :// www.kaggl e.com/nabee lsaji d917/covid -19-x-ray-10000 –image s, online: accessed 20 April 2020
    22. Kanne JP (2020) Chest ct findings in 2019 novel coronavirus (2019-ncov) infections from wuhan, china: key points for the radiologist [23]Kim H (2020) Outbreak of novel coronavirus (covid19): What is the role of radiologists?
    23. Krizhevsky A, Sutskever I, Hinton G (2012) Imagenet classification with deep convolutional neural networks. Neural Inform Process Syst 25
    24. Krzanowski WJ, Hand DJ (2009) ROC curves for continuous data. Crc Press
    25. Lai CC, Shih TP, Ko WC, Tang HJ, Hsueh PR (2020) Severe acute respiratory syndrome coronavirus 2 (sars-cov-2) and corona virus disease2019 (covid-19): the epidemic and the challenges. Int J Antimicrobial Agents p 105924
    26. Lalkhen AG, McCluskey A (2008) Clinical tests: sensitivity and specificity. Continuing Education in Anaesthesia Critical Care & Pain 8(6):221–223
    27. Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324
    28. Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, Bai J, Lu Y, Fang Z, Song Q, et al. (2020) Artificial intelligence distinguishes covid- 19 from community acquired pneumonia on chest ct. Radiology p 200905
    29. Lippi G, Simundic AM, Plebani M (2020) Potential preanalytical and analytical vulnerabilities in the laboratory diagnosis of coronavirus disease 2019 (covid-19). Clin Chem Lab Med (CCLM) 1(aheadof-print)
    30. Long T (2019) Research on application of athlete gesture tracking algorithms based on deep learning. Journal of Ambient Intelligence and Humanized Computing pp 1–9, https ://doi. org/10.1007/s1265 2- 019-01575 –w
    31. Mining WID (2006) Data mining: concepts and techniques. Morgan Kaufinann 10:559–569 [33]Narin A, Kaya C, Pamuk Z (2020) Automatic detection of coronavirus disease (covid-19) using xray images and deep convolutional neural networks. arXiv preprint arXiv :20031 0849
    32. Nielsen MA (2015) Neural Netw Deep Learn, vol 25. Determination press, San Francisco
    33. Organization WH (2020) Coronavirus (covid-19). https ://covid 19.who.int/, online: accessed 27 April 2020

Recent Article