Artificial Intelligent Techniques Applied for Detection Covid-19 Based on Chest Medical Imaging

Abstract

One of the ways to detect COVID is x-rays, computerized tomography (CT). This paper aims to detect COVID-19 without any user intervention. The proposed algorithm consists of 5 stages to detect and classify COVID-19 from cross-sectional images. These stages include; The first of these stages is to collect data from hospitals as real data and from the Internet for the injured and other healthy people, then the pre-noise removal stage and convert it from RGB to grayscale, then we improve the image, segmentation and formalities, the other stage is a stage used to extract important characteristics, and the last stage is classification CT images using FFBPN and SVM and compare the result between them and see if the person is infected or healthy. This study was implemented in Matlab software. The results showed that the noise cancellation technology using anisotropic filtering gave the best results. As for the optimization technology, only the brightness of the images has been increased. At the stage of segmentation of the area of lung injection using the area transplant method, the best results are the detection of COVID-19 from other healthy tissues. The FFBPN has given the best results for detecting and classifying COVID-19 as well as determining whether a person has been infected or not. The results of the proposed methodology in accurate and rapid detection of COVID-19 in the lung.