Abstract
The emergence of COVID-19 disease in the world has moved the wheel of scientific research in order to detect it in the best method, and the fastest of these methods is the use of Artificial Intelligence (AI) techniques to help medical professionals detect COVID-19. The proposed topic is aim to develop algorithm based on combination between imageprocessing techniques with artificial intelligence to diagnose COVID-19. The proposed algorithm consists of five stages to detect and classify COVID-19 from Computer Tomography (CT) images. These stages include; The first of these stages is to collect data from hospitals as real data and from Kagglewebsite for patients and healthy people, then the stage before removing the noise and converting it from RGB to grayscale, then we improve the image, segmentation and formalities, the other stage is a stage used to extract the important characteristics, and the last stage is the classification of images CT scan using Feed Forward Back Propagation Network (FFBPN) and Support Vector Machine (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 detection of COVID-19 from other healthy tissues. The FFBPN gave 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. The contribution of this paper is to help medical staff detect COVID-19 without human intervention.
Highlights
The COVID-19 epidemic, which began on December 31, 2019 in Wuhan, Hubei Province, China, is rapidly declared a pandemic [1,2,3]
Automatic scanning with portable Computed Tomography (CT) scanning platform supported with visual Artificial Intelligence techniques are a prime example [8]
These stages include; The first of these stages is to collect data from hospitals as real data and from Kaggle website for patients and healthy people, the stage before removing the noise and converting it from RGB to grayscale, we improve the image, segmentation and formalities, the other stage is a stage used to extract the important characteristics, and the last stage is the classification of images CT scan using Feed Forward Back Propagation Network (FFBPN) and Support Vector Machine (SVM )and compare the result between them and see if the person is infected or healthy, as shown in Figure 1 main block diagram of the proposed algorithm and the stages of wok
Summary
The COVID-19 epidemic, which began on December 31, 2019 in Wuhan, Hubei Province, China, is rapidly declared a pandemic [1,2,3]. To the United States of America, [5] These viruses infected animals, transmitted to humans this virus (SARS-CoV), known as Middle East Respiratory Syndrome (MERS-CoV), which infects the acute respiratory system and leads to human deaths [6]. Symptoms of this virus are fever, cough, fatigue, sore throat, headache, body aches and shortness of breath. Many contactless imaging techniques have been developed during the outbreak of the COVID-19 virus [7, 8, 9,10], including the use of the examination rooms of surveillance cameras [11,12, 13] or their use of the system [14] and the use of portable Computer Tomography(CT) [ 8,15-18). To prevent any unwanted contact between technicians and patients, every room has a private entrance
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