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

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

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.

• Journal Article Type: Article
• Acceptance Date: Jun 2, 2020
• Online Publication Date: Jun 12, 2020
• Publication Date: 2020
• Deposit Date: Jul 23, 2020
• Publicly Available Date: Jul 23, 2020
• Journal: IEEE Access
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 8
• Pages: 109581-109595
• DOI: https://doi.org/10.1109/access.2020.3001973
• Keywords: Artificial intelligence, big data, bioinformatics, biomedical informatics, COVID-19, deep learning, diagnosis, machine learning, treatment
• Public URL: http://researchrepository.napier.ac.uk/Output/2677593