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@article{154244,
        author = {Arun  Raj  S and Anand.  S. B. and Fathima  B. and Ponnu  Raj  R.},
        title = {Covid-19 Detection  from  Chest  X-Ray using ACGAN and  RESNET},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {7},
        pages = {121-126},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=154244},
        abstract = {COVID-19 is a viral infection  brought about by Coronavirus 2 (SARS-CoV-2). The spread of COVID-19 appears to have a hindering impact on the worldwide Economy  and wellbeing.  A positive chest X-beam of contaminated  patients is a urgent advance in the fight against COVID-19. This has prompted the presentation of an assortment of profound learning frameworks and studies have shown that the exactness  of COVID-19  patient recognition using chest X-beams is unequivocally idealistic.  Profound  learning  organizations  like convolutional  neural  organizations  (CNNs) need a significant  measure  of preparing information.  In this task, we present a technique to create engineered chest X-beam  (CXR) pictures by fostering an Auxiliary Classifier Generative Adversarial Network (ACGAN)  based Model called Covid GAN. Also, the proposed framework shows that the engineered pictures created from Covid GAN can be used to improve the exhibition of CNN based design called Resnet.},
        keywords = {},
        month = {},
        }