Open AccessDetection and Analysis of COVID-19 in Chest X-RAY Images
Author(s) -
Sheikh Raheela Gaffa,
Amit Gupta
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.40520
Subject(s) - covid-19 , medical diagnosis , preprocessor , artificial intelligence , radiological weapon , computer science , binary classification , radiological imaging , medicine , medical imaging , pattern recognition (psychology) , radiology , disease , pathology , support vector machine , outbreak , infectious disease (medical specialty)
Abstract: COVID-19 is the human body's most deadly and deadliest illness caused by a single coronavirus. In December 2019, the Coronavirus, which is thought to have originated in Wuhan, China and is responsible for a large number of deaths, spread rapidly around the world. Early detection of COVID-19 by correct diagnosis, especially in cases when there are no obvious symptoms, may help patients live longer. Chest X-rays and CT scans are the most used diagnostic methods for this illness. According to this study, COVID-19 may be recognized using a machine vision approach from chest X-ray images and CT scans. According to current research based on radiological imaging techniques, such images provide crucial information about the COVID-19 virus. . This proposed solution, which employs contemporary artificial intelligence (AI) tools, has been demonstrated to be successful in detecting COVID-19, and when paired with radiological imaging, can help in the accurate diagnosis of this disease. . In binary classification, the proposed technique is meant to provide accurate diagnoses for COVID and non-COVID patients. With 98.87 percent accuracy in network comparisons and 95.91 percent accuracy in patient status classification, the findings show that VGG-16 is the best architecture for the reference dataset. Convolutional layers were used, with each layer having its own filtering. As a consequence, the VGG-16 design was successful in classifying COVID-19 cases. This design, however, may be improved significantly by altering it or adding a preprocessing step on top of it. Our technology may be used to assist radiologists in validating their first screening and can also be utilized to swiftly screen patients through the cloud. Keywords: Covid 19, Disease detection, X Ray, VGG-16