![]() The motivation of this study is to design a fast, accurate, and automated method for COVID-19 detection using the CT scans. These methods can help physicians speed up the analysis, arrive at a more accurate diagnosis, and develop appropriate treatment. These techniques are used in various medical diagnostic applications such as lung nodules classification based on CT images, heart rhythm monitoring, brain tumor classification from MRI images, and breast cancer detection using histopathology images. Īt present, image processing, computer vision, and artificial intelligence (AI) have been extensively used in medical imaging and digital health applications due to their excellent performance on image classification and target detection. Some patients may have air bronchogram, bronchial wall thickening, lung nodules, pleural effusion, pleural thickening, lymphadenopathy, and other abnormalities. The main chest CT manifestations of COVID-19 are bilateral, peripheral/subpleural, posterior ground-glass opacity, crazy paving pattern, and consolidation. In, it is shown that for suspected cases with a negative nucleic acid test, chest CT examination is necessary to improve the accuracy of diagnosis. The study in found that among 1014 suspected cases, the positive rate of RT-PCR test and lung CT was 59% and 88%, respectively, indicating that lung CT has a higher sensitivity to COVID-19. The research in showed that the joint detection of nucleic acid and antibodies could increase the true positive rate of COVID-19. The inadequate sensitivity of the RT-PCR test may result in false negatives and more potential infections. The rapid COVID tests and self-tests can miss some cases. However, NGS and RT-PCR tests are accurate only when properly performed by health care professionals. The “Next-Generation” sequencing (NGS) and reverse transcription-polymerase chain reaction (RT-PCR) test are the most commonly used methods for COVID-19 detection. Severe cases may rapidly develop into acute respiratory distress syndrome (ARDS), sepsis, and renal failure. Some cases may have a sore throat, chest pain, myalgia, and diarrhea. The main clinical manifestations are dry cough, fever, fatigue, and dyspnea. ĬOVID-19 has many similarities with common respiratory viral infections. The cumulative number of COVID-19 infections worldwide has exceeded 110 million, and the death toll has stood over 2.6 million. The World Health Organization (WHO) declared the new type of coronavirus infection as a Public Health Emergency of International Concern (PHEIC) on January 31, 2020. In December 2019, unexplained illness attacked Wuhan, which was subsequently confirmed to be caused by a novel coronavirus called SARS-CoV-2, and the infection caused by it was named COVID-19. The proposed deep learning models and software tool can be used by radiologist to diagnose COVID-19 more accurately and efficiently. In addition, we also developed a freely accessible online simulation software for automated COVID-19 detection using CT images. This heatmap method is helpful for a radiologist to identify the abnormal pattern of COVID-19 on chest CT images. Moreover, a heatmap method to highlight the lesion area on COVID-19 chest CT images is introduced in the paper. It is also shown that the accuracy is improved by around 1% by using the 2D global max pooling layer. Based off of metric such as area under curve (AUC), sensitivity, specificity, accuracy, and false discovery rate (FDR), experimental results show that the proposed models outperform the previous methods, and the best model achieves an area under curve of 0.9744 and accuracy 94.12% on our test datasets. We compare the proposed models to the existing state-of-the-art deep learning models such as CNN based models and vision transformer (ViT) models. The proposed models are based on the state-of-the-art deep convolutional neural network (CNN) architecture, and a 2D global max pooling (globalMaxPool2D) layer is used to improve the performance. This study introduces a novel method for fast and automated COVID-19 diagnosis using the chest CT scans. ![]() However, a complete CT-scan has hundreds of slices, and it is time-consuming for radiologists to check each slice to diagnose COVID-19. Chest computed tomography (CT) is one of the most commonly used diagnosis methods. Fast and accurate diagnostic methods for COVID-19 detection play a vital role in containing the plague. Starting from December 2019, the global pandemic of coronavirus disease 2019 (COVID-19) is continuously expanding and has caused several millions of deaths worldwide.
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