Abstract
The purpose of this chapter is to illustrate how artificial intelligence (AI) technologies have been used for COVID-19 detection and analysis. Specifically, the use of neural networks (NN) and machine learning (ML) are described along with which countries are creating these techniques and how these are being used for COVID-19 diagnosis and detection. Illustrations of multi-layer convolutional neural networks (CNN), recurrent neural networks (RNN), and deep neural networks (DNN) are provided to show how these are used for COVID-19 detection and prediction. A summary of big data analytics for COVID-19 and some available COVID-19 open-source data sets and repositories and their characteristics for research and analysis are also provided. An example is also shown for artificial intelligence (AI) and neural network (NN) applications using real-time COVID-19 data.
TopMachine Learning And Covid-19
Machine learning (ML) is based on the premise that an intelligent machine should be able to learn and adapt from its environment based on its experiences without being explicitly programmed. The availability of open-source data sets with COVID-19 data allows the experimentation of using machine learning techniques and deep neural networks for the prediction and diagnosis of COVID-19 using Computed Tomography (CT) scans and x-rays. CT scans show detailed images of any part of the body, including the bones, muscles, fat, organs and blood vessels.
Shuja et al. (2020) provided a comprehensive survey of open-source data sets that included categories of biomedical images, textual, and speech data. As COVID-19 test kits are in short supply, medical image-based diagnosis provides an alternative method of COVID-19 diagnosis. According to Shuja et al. (2020), the combination of artificial intelligence (AI) and open-source data sets practical solution for COVID-19 diagnosis that can be implemented in hospitals worldwide.
According to the World Health Organization (WHO) (2020) some of the leading hospitals across the world are utilizing artificial intelligence and machine learning algorithms to diagnose COVID-19 cases using Computed Tomography (CT) scans and X-ray images.
Key Terms in this Chapter
H2O AutoML: Learning algorithm within H 2 O open-source distributed machine learning platform that overlooks the process of finding candidate models using large datasets ( Marques et al., 2021 AU98: The citation "Marques et al., 2021" matches multiple references. Please add letters (e.g. "Smith 2000a"), or additional authors to the citation, to uniquely match references and citations. ).
Recurrent Neural Networks (RNN): Class of Artificial Neural Networks (ANN) also called Feedback Neural Networks (FNN) where connections between nodes form a directed graph along a temporal sequence ( Wikipedia, 2020 ).
Deep Neural Networks: Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw input.
Keras: Keras is an open-source software library that provides a Python interface for artificial neural networks. It is a High-level Python neural network library that runs on the top layer of TensorFlow ( Hanfi, 2020 ).
Long Short-Term Memory (LSTM): A type of Recurrent Neural Network (RNN) capable of learning order dependence in sequence prediction problems as a behavior required in complex problem domains like machine translation, speech recognition, and more ( Brownlee, 2017 ).
Tensorflow: Open-source software library originally developed by Google Brian Team for numerical computation using data flow graphs. TensorFlow is a free and open-source software library for machine learning. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks ( Wikipedia, 2021c ).
COVID-19: Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first case was identified in Wuhan, China, in December 2019.
Convolutional Neural Networks (CNN): In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery.