Abstract
In the present paper, an CNN algorithm is implemented to detect whether the patients are effected with Covid, a huge pandemic that tainted a big measure of the human population in the ï¬rst part of the year 2020. Moreover, the investigation introduced and employed 9 popular Convolutional Neural Networks (CNNs) for the classiï¬cation of images of X-ray starting from victims with COVID, pneumonia, and healthy people. Exploration evidence showed that the CNNs can possibly identify respiratory sicknesses with high accuracy, even a large number of sample pictures should be grouped. Particularly, VGG16, Scores a 95% overall accuracy. The big values related with affectability, speciï¬city, and exactness of Corona virus strain, shows the capacity of these models to identify positive or potentially negative covid-19 cases perfectly to reduce as much as could reasonably be expected the infection to spread into the network. As the outputs show, deciding the best model for this classiï¬cation task involves major exhibition measurements. Moreover, one of the empowering results is the ability of the previously mentioned CNNs to accomplish high affectability and accuracy on the healthy class along these lines guaranteeing the minimization of false positives with respect to contamination cases which can deeply help to reduce the weight on the healthcare framework.
Keywords
covid 19, neural networks, deep learning, corona virus
How to Cite:
Adusumalli Harish , K.Prem Kumar , DETECTION OF COVID 19 USING CHEST X-RAY WITH VGG 16 NEURAL NETWORK , Volume 6 , Issue 12, december 2021, EPRA International Journal of Research & Development (IJRD),