COVID-19 Detection and Analysis Using Chest X-Ray and CT- Scan Images

Abstract

Abstract: COVID-19 seems to be the most devastating and lethal illness characterized by an unique coronavirus for the human body. Coronavirus, which is considered to have originated in Wuhan, China, and is responsible for a huge number of deaths, spread swiftly around the world in December 2019. Early discovery of COVID-19 by proper diagnosis, especially in situations with no evident symptoms, could reduce the death rate of patients. The primary diagnostic tools for this condition are chest Xrays and CT scans. COVID-19 may be detected using a machine vision technique from chest X-ray pictures and CT scans, according to this study.The model's performance was evaluated using generalised data throughout the testing step. According to recent studies gained using radiological imaging techniques, such images convey crucial data about the COVID-19 virus. This proposed approach, which makes use of modern artificial intelligence (AI) techniques, has shown to be effective in recognising COVID-19, and when combined with radiological imaging, can aid in the correct detection of this disease. The proposed approach was created in order to provide accurate assessments for COVID and non-COVID patients.The results demonstrate that VGG-16 is the best architecture for the reference dataset, with 98.87 percent accuracy in network evaluations and 95.91 percent success in patient status identification. Convolutional layers were developed, with distinct filtering applied to each layer. As a result, the VGG-16 design performed well in the classification of COVID-19 cases. Nevertheless, by modifying it or adding a preprocessing step on top of it, this architecture allows for significant gains. Our methodology can be used to help radiologists validate their first screenings and can also be used to screen patients quickly via the cloud.