Convolutional Neural Network-based Covid-19 Analysis with Internet of Things

Abstract

The very hazardous respiratory illness known as COVID-2 (SARS-CoV-2), which is the root cause of the even more serious illness known as COVID-19, was caused by the COVID-2 virus. The COVID-19 virus was identified in Wuhan City, China, in the month of December in 2019. It began in China and then spread to other parts of the world before it was officially classified as a pandemic. It has had a significant impact on day-to-day life, the welfare of people in general, and the economy of the whole globe. It is of the utmost importance, particularly in the beginning stages of treatment, to pinpoint the constructive experiences that are useful at the proper time. The identification of this virus involves a substantial number of tests, each of which takes a certain amount of time; nevertheless, there are currently no other automated tool kits that can be used in their place. X-ray photos of the chest that are obtained via the use of radiology imaging methods may provide significant insight into the COVID-19 infection if they are analysed carefully. An accurate diagnosis of the infection may be obtained via the application of deep learning techniques, which are applied to radiological images and make use of cutting-edge technology such as artificial intelligence. Patients who reside in distant places, where it may not be feasible for them to have rapid access to medical facilities, may benefit from this kind of analysis throughout the course of their therapy. One of the deep learning strategies that are used in the creation of the model that has been proposed is the use of convolutional neural networks. The images of chest X-rays are analysed by these networks to detect whether a person has a positive or negative result for the Covid gene.