Abstract
COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19’s spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long/Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Artificial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications.
Highlights
Jamshidi et al.: Artificial Intelligence (AI) and COVID-19: Deep Learning Approaches for Diagnosis and Treatment known as COVID-19 which proved itself as a tricky illness that can emerge in various forms and levels of severity ranging from mild to severe with the risk of organ failure and death
Considering that Imaging workflows can inspire advances in machine learning methods capable of assisting radiologists who seek an analysis of complex imaging and text data, we described models that can analyze medical imaging facilitating the completion of a process that recognizes COVID-19-related infections [54]
The introduced conceptual structures and platforms in the research field of AI-based techniques, which are suitable for dealing with COVID-19 issues, have been studied in this paper
Summary
In areas hit by the epidemy, negative RT-PCR but positive CT features are significant signs of COVID-19 and can highlight the importance of rapid detection of the infection that gives the community as well as clinicians a better chance to bring the viral spread under control [52] While radiological examinations such as computed tomography CT has been demonstrated as effective methods for screening and diagnosis, there is evidence that considerable numbers of radiologists and technologists have been infected while serving COVID-19 patients [50]. A GAN network to predict viral gastrointestinal infection probability can be done through the extraction of the feature from these images to help patients in the process of their treatment. Screening of COVID-19 patients seems to be effectively managed through DL models demonstrated in this study that can be an effectively helpful supplementary diagnostic method for clinical doctors in close contact with patients [74]
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