Open AccessData Augmentation using Auxiliary Classifier for Improved Detection of Covid 19
Author(s) -
Lakshmisetty Ruthvik Raj,
Bitra Harsha Vardhan,
Mullapudi Raghu Vamsi,
Keerthikeshwar Reddy Mamilla,
Poorna Chandra Vemula
Publication year - 2021
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c6386.0910321
Subject(s) - convolutional neural network , covid-19 , leverage (statistics) , computer science , transfer of learning , deep learning , artificial intelligence , classifier (uml) , pattern recognition (psychology) , machine learning , medicine , infectious disease (medical specialty) , disease , pathology , outbreak
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 Covid 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.