![]() Also, it has nearly collapsed health systems in many countries, thus it has been declared by WHO as a global pandemic. The disease has worsened both human health worldwide and the world’s economic health. However, the coronavirus, a commonly used name for the disease caused by a new novel coronavirus caused by SARS-CoV-2, was first seen in Wuhan, China in December 2019. According to the data of the World Health Organization (WHO), 450 million pneumonia cases are recorded every year in the world. The disease can be seen at any age, but children under 2 years of age, people with very low immune systems, and people over 65 years of age are at high risk. People at high risk of having pneumonia are older than 65 or younger than 2 years of age or already have health problems. Moreover, the chest X-ray is the main tool in diagnosis. Traditionally, pneumonia is diagnosed through reviewing the patient’s medical history, physical exam, and other diagnostic tests like a blood test, sputum test, pulse oximetry test. It causes the air sacs, or alveoli, of the lungs to fill up with fluid or pus. Pneumonia is swelling and possible inflammation of the tissue in one or both lungs. The infection might be a sign of various diseases such as pneumonia, and nowadays the world’s nightmare coronavirus. The tissue of the lungs can be attacked by bacteria, viruses, and rarely parasites, as result, a lung infection may occur. Their function in the respiratory system is to extract oxygen from the atmosphere and transfer it into the bloodstream, and to release carbon dioxide from the bloodstream into the atmosphere, in the process of gas exchange. The lungs are among the primary organs of humans’ respiratory systems and are located near the backbone on either side of the heart. According to the simulation results, the proposed model is promising, can quickly and accurately classify chest images, and helps doctors as the second reader in their final decision. Conversely, the model’s test set metrics such as average accuracy, average recall, and average precision are 97.78%, 96.67%, and 96.67%, respectively. Moreover, the average training false-positive and false-negative rates are 0.0085 and 0.0171, respectively. The model achieves an average training accuracy of 0.9886, an average training recall of 0.9829, and an average training precision of 0.9837. The model was developed by two public datasets: Cohen dataset and Kermany dataset. The model that inputs chest X-ray images is capable of extracting radiographic patterns on chest X-ray images to turn into valuable information and monitor structural differences in the lungs caused by the diseases. Herein, a transfer learning-based multi-class convolutional neural network model was proposed for the automatic detection of pneumonia and also for differentiating non-COVID-19 pneumonia and COVID-19. Thus, the need for radiologists has been increased considerably not only to detect COVID-19 but also to identify other abnormalities it caused. The chest X-ray is the main reference in diagnosing pneumonia. The symptoms of pneumonia are alike, and COVID-19 is no exception. Also, early detection of COVID-19 is crucial to keep its morbidity and mortality rates low. In most regions of the world, COVID-19 test is not possible due to the absence of the diagnostic kit, even if the kit exists, its false-negative (giving a negative result for a person infected with COVID-19) rate is high. The rate of increase is high, and the world got caught the disease unprepared. Nowadays, the world is fighting against the new coronavirus: COVID-19. Most people may instantly encounter coronavirus in their life and may result in pneumonia. Coronavirus-caused diseases are common worldwide and might worsen both human health and the world economy.
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