Deep Pre-Trained Convolutional Neural System for High-Accuracy Covid19 Forecasting from Chest X-Rays

The continuous battle against the variants of Corona Virus demands speedy treatment and quick diagnostic reporting on priority basis. With millions of people contracting the infection every day and a mortality rate of 2%, our goal is to solve this growing problem by developing an important and substantive method for diagnosing COVID19 patients. Due to a proportionally reduced number of medical practitioners, testing kits, and other resources in densely populated nations, the exponential development of COVID19 cases is having a significant impact on the health care system, making it increasingly important to identify infected patients. The goal of this work is to develop an exact, productive and time-saving algorithm to identify positive corona patients that addresses the aforementioned issues. In this paper, a Deep Convolution Neural Network model called "EfficientNet"is implemented and explored that can reveal significant diagnostic characteristics to enable radiologists and medical specialists locate COVID-19 infected patients using X-ray pictures of the chest and aid in the fight against the pandemic. The experimental findings conclusively indicate that an accuracy rate of 99.71 percent was obtained for binary classification of Non-COVID and COVID Chest X-ray pictures. Our pretrained Deep Learning classification model can be a significant contribution to recognizing COVID-19 inflicted individuals due to its high diagnostic accuracy. © 2022 IEEE.

Detection of COVID-19 Cases from Chest Radiography Images

The COVID-19 epidemic continues to have a devastating influence on the global population's well-being and economy. One of the most important advances in the fight against COVID-19 is thorough screening of infected individuals, with radiological imaging using chest radiography being one of the most important screening methods. Early studies revealed that patients with abnormalities in chest radiography images were infected with COVID-19. Persuaded by this, a variety of computerized reasoning and simulated intelligence frameworks based on profound learning have been suggested, with promising results in terms of precision in differentiating COVID-infected individuals. COVID-Net, a neural system configuration custom-fit for the recognition of COVID-19 instances from chest radiography photographs that is open source and accessible to the general public, is presented in this study. Many techniques have been used for the detection of COVID-19, but here we are going to focus on the chest radiography technique with the application of machine learning and image processing concepts. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.