Data Augmentation using Auxiliary Classifier for Improved Detection of Covid 19

Lakshmisetty Ruthvik Raj, Department of Computer Science, Vellore Institute of Technology, Vellore (Tamil Nadu), India., Bitra Harsha Vardhan, Mullapudi Raghu Vamsi, Keerthikeshwar Reddy Mamilla, Poorna Chandra Vemula, Department of Computer Science, Vellore Institute of Technology, Vellore (Tamil Nadu), India., Department of Computer Science, Vellore Institute of Technology, Vellore (Tamil Nadu), India., Department of Computer Science, Vellore Institute of Technology, Vellore (Tamil Nadu), India., Department of Computer Science, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
2021 International journal of recent technology and engineering  
COVID-19 is a severe and potentially fatal respiratory infection called coronavirus 2 disease (SARS-Co-2). COVID-19 is easily detectable on an abnormal chest x-ray. Numerous extensive studies have been conducted due to the findings, demonstrating how precise the detection of coronas using X-rays within the chest is. To train a deep learning network, such as a convolutional neural network, a large amount of data is required. Due to the recent end of the pandemic, it is difficult to collect many
more » ... ovid x-ray images in a short period. The purpose of this study is to demonstrate how X-ray imaging (CXR) is created using the Covid CNN model-based convolutional network. Additionally, we demonstrate that the performance of CNNs and various COVID-19 acquisition algorithms can be used to generate synthetic images from data extensions. Alone, with CNN distribution, an accuracy of 85 percent was achieved. The accuracy has been increased to 95% by adding artificial images generated from data. We anticipate that this approach will expedite the discovery of COVID-19 and result in radiological solid programs. We leverage transfer learning in this paper to reduce time complexity and achieve the highest accuracy.
doi:10.35940/ijrte.c6386.0910321 fatcat:v3ld7koemvayddlqy5jbzpsrni