Design of an intrinsically scalable model for medical disease identification using hybrid swarm intelligence with augmented transfer learning

Abstract

People's lives could be in danger if a contagious disease spreads quickly, Corona-2019 virus disease (COVID-19) is one. The coronavirus epidemic rapidly spread over the world. The Corona virus has had a major impact on the health of populations and healthcare systems all over the world. RT-PCR (RT-Reverse transcription, PCR-polymerase chain reaction) testing can benefit from the use of computed tomography images. Most available methods use large training data, and the detection accuracy needs to be improved due to the inadequate border segment of symptom descriptions. This study proposes a robust and effective way for identifying normal and COVID-19 patients using small training data. Deep learning quickly creates accurate models. Data augmentation increases the training dataset to reduce over fitting and improve model generalisation. Using data augmentation, we evaluated Xception and VGG-19. The study showed that deep learning can detect COVID-19.