A New Deep Convolutional Neural Network Learning Model for Covid-19 Diagnosis

Paper Title

A New Deep Convolutional Neural Network Learning Model for Covid-19 Diagnosis

Authors

Henry Li 1 and Chen 2, 1 Princeton International School of Mathematics and Science 19 Lambert Dr, USA, 2 Chinese Academy of Science, Academy of Mathematics and System Science 55 ZhongGuanCun, China

Abstract

Ever since 2019, people from all over the world are talking about infection with SARS-CoV-2, also known as COVID-19. The symptoms range from asymptomatic conditions to fatal disease, with lung injury most frequently being the result of it. As time flies during the pandemic, the role of medical imaging has been more cortical than ever, with computed tomography being an alternative testing method combined with polymerase chain reaction testing to have a broader role. However, only performing medical imaging testing with suspected patients without classifying whether or not the patient has COVID-19 is not practical. Nevertheless, in many different areas, excellent pulmonology doctors are in extreme shortage for most of the developing counties. Even in developed countries, doctors are too busy with diagnosing and curing patients, so the need for classification for the medical image of patients to see whether they have COVID-19 or not is of high necessity. Moreover, studies of chest radiographs and CT images with applications of artificial intelligence have shown of big importance and necessity. In this paper, we mainly focus on the usage of deep learning and machine learning for categories with findings typical COVID-19 infected images and an application of medical images and testing of the system.

Keywords

SARS-CoV-2, COVID-19, Lung injury, Medical imaging, pulmonology, Computed tomography, Polymerase chain reaction, Imaging classification, Deep learning, Machine learning

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