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Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment - 2020

Artificial Intelligence And Covid-19: Deep Learning Approaches For Diagnosis And Treatment

Research Area:  Machine Learning

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.

Keywords:  

Author(s) Name:   Mohammad Jamshidi; Ali Lalbakhsh; Jakub Talla; Zdeněk Peroutka; Farimah Hadjilooei; Pedram Lalbakhsh; Morteza Jamshidi; Luigi La Spada; Mirhamed Mirmozafari; Mojgan Dehghani; Asal Sabet; Saeed Roshani; Sobhan Roshani; Nima Bayat-Makou; Bahare Mohamadzade; Zahra Malek; Alireza Jamshidi; Sarah Kiani; Hamed Hashemi-Dezaki; Wahab Mohyuddin

Journal name:  IEEE Access

Conferrence name:  

Publisher name:  IEEE

DOI:  10.1109/ACCESS.2020.3001973

Volume Information:  ( Volume: 8) Page(s): 109581 - 109595