Covid-19 Detection from Chest X-Ray using ACGAN and RESNET - S, Arun Raj; SB, Anand; B, Fathima; Raj R., Ponnu (International Journal of Computer Sciences and Engineering, 2021)
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.