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  Published Paper Details:

  Paper Title

DETECTION AND CLASSIFICATION OF COVID-19 AND OTHER LUNG DISEASES USING DEEP LEARNING - A SURVEY

  Authors

  Mayuresh Deshmukh,  Utkarsh Dhavale,  Ashitosh Dhone,  Pratik Hegde,  Mahalakshmi Bodireddy

  Keywords

Deep Learning, Transfer Learning, COVID-19, Pneumonia, Tuberculosis, Chest CT images, Computer vision, Convolutional Neural Network.

  Abstract


This paper describes different methods used for detection of COVID-19 and other lung diseases like Pneumonia, Tuberculosis. Currently no vaccine is approved for coronavirus disease. So testing the people is of immense importance for detection of COVID-19. There are various techniques used for the detection of Coronavirus. RT-PCR, Chest X-ray, Lung CT scan images are some of those techniques. Here, we describe and compare various techniques that are used for testing of different lung diseases like COVID-19, Pneumonia, Tuberculosis. The time taken for COVID-19 testing is also crucial since the infected people are acting like coronavirus carriers. So now as the cases are increasing, improving the testing speed with more accuracy is very important. Deep Learning techniques are used to automate the testing process and are also helping in increasing the accuracy. In this survey paper, we study and compare various deep learning techniques for detection of Coronavirus(COVID-19) and other lung diseases.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2105157

  Paper ID - 206655

  Page Number(s) - b436-b440

  Pubished in - Volume 9 | Issue 5 | May 2021

  DOI (Digital Object Identifier) -    http://doi.one/10.1729/Journal.27550

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Mayuresh Deshmukh,  Utkarsh Dhavale,  Ashitosh Dhone,  Pratik Hegde,  Mahalakshmi Bodireddy,   "DETECTION AND CLASSIFICATION OF COVID-19 AND OTHER LUNG DISEASES USING DEEP LEARNING - A SURVEY", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.9, Issue 5, pp.b436-b440, May 2021, Available at :http://www.ijcrt.org/papers/IJCRT2105157.pdf

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ISSN: 2320-2882
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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