Agarwal, Vertika and Lohani, M. C. and Bist, Ankur Singh and Harahap, Eka Purnama (2022) Analysis Of Deep Learning Techniques For Chest X-Ray Classification In Context Of Covid-19. ADI Journal on Recent Innovation (AJRI), 3 (2). pp. 1-9. ISSN 2686-0384

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Abstract

Coronaviruses (COV) are a large family of viruses that cause illness ranging from common cold to more severe disease such as MIDDLE EAST RESPIRATORY SYNDROME (MERS-COV) and SEVERE ACUTE RESPIRATORY SYNDROME (SARS-COV). Common signs of infection include respiratory symptoms, Fever, Cough, Shortness of breath and breathing difficulties. In severe cases, infection can cause pneumonia, severe acute respiratory syndrome, kidney failure and even death.3-Tier strategy is employed by government to combat this virus i.e., Track, Test and Treat. So, there is a need to increase the testing speed but the main stumbling block is the time RT-PCR takes which is around 2-3 days. In this situation, the recent research using Radiology imaging (such as Xray) techniques can be proven helpful to detect Covid 19. Latest deep learning techniques applied to Xray scans which rapidly detects the disease and thus reducing the time for testing. Moreover, it is accurate as compare to RT-PCR test where nose and mouth swabs are taken by lab technician which is prone to error.In this survey paper, ten different DL Techniques are surveyed which performs Xray classification with different accuracy. Different combination of Datasets are employed by these algorithms to improve the performance of their proposed model.Our paper evaluates the performance of each algorithm based on two parameters -Accuracy and Sensitivity.

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