AI-driven deep learning method for diagnosing COVID-19 symptoms

Abstract

The outbreak of COVID-19 put the whole world in an unprecedentedly harsh situation, horribly disrupting life around the world and killing thousands. COVID-19 remains a real threat to the public health system as it spreads to 212 countries and territories and the number of cases of infection and deaths increases to 5,212,172 and 334,915 (as of May 22, 2020). This treatise provides a response to virus eradication via artificial intelligence (AI). Several deep learning (DL) methods have been described to achieve this goal, including GAN (Generative Adversarial Network), ELM (Extreme Learning Machine), and LSTM (Long / Short Term Memory). It describes an integrated bioinformatics approach that combines various aspects of information from a series of orthopedic and unstructured data sources to form a user-friendly platform for physicians and researchers. A major advantage of these AI-powered platforms is to facilitate the diagnosis and treatment process of the COVID-19 disease. The latest relevant publications and medical reports have been investigated to select inputs and targets for networks that will facilitate arriving at reliable artificial neural network-based tools for COVID-19-related challenges. There are also several specific inputs per platform, including clinical data and data in various formats, such as medical images, which can improve the performance of the introduced method for the best response in real application.