A Survey on Deep Learning and Machine Learning for COVID-19 Detection

Coronaviruses are a type of virus that can cause a variety of disorders and exist in different types. COVID-19 is derived from a special type of a respiratory illness caused by the SARS-CoV-2 virus, discovered in 2019. Approximately two years ago, COVID-19 was discovered in the Chinese city of Wuhan, and it has since become a worldwide source of concern. A COVID-19 confirmed patient is experiencing symptoms such as fever, fatigue, and a dry cough. Based on the results of laboratory tests and/or chest X-rays, the COVID-19 diagnosis is established. When it comes to research using chest CT scans /X-ray for the diagnosis of COVID-19, which is based on medical imaging, Artificial Intelligence (AI) approaches are increasingly being applied in a variety of ways. Machine learning and deep learning are fields of artificial intelligence that can be used to analyze the data that was acquired in order to better understand the origins of COVID-19. The outcomes of applying such an approach will aid in a better understanding of the nature of the threat and how it might be mitigated. For this reason, this work gives an overview of deep learning and machine learning approaches for the detection of COVID-19. Several COVID-19 detection methods are discussed in detail, as well as the issues, current challenges associated with artificial intelligence and medical researchers' approaches to providing a comprehensive assessment of detecting COVID-19. © 2021 ACM.

An improved deepNN with feature ranking for Covid-19 detection

The outbreak of Covid-19 has taken the lives of many patients so far. The symptoms of COVID-19 include muscle pains, loss of taste and smell, coughs, fever, and sore throat, which can lead to severe cases of breathing difficulties, organ failure, and death. Thus, the early detection of the virus is very crucial. COVID-19 can be detected using clinical tests, making us need to know the most important symptoms/features that can enhance the decision process. In this work, we propose a modified multilayer perceptron (MLP) with feature selection (MLPFS) to predict the positive COVID-19 cases based on symptoms and features from patients’ electronic medical records (EMR). MLPFS model includes a layer that identifies the most informative symptoms to minimize the number of symptoms base on their relative importance. Training the model with only the highest informative symptoms can fasten the learning process and increase accuracy. Experiments were conducted using three different COVID-19 datasets and eight different models, including the proposed MLPFS. Results show that MLPFS achieves the best feature reduction across all datasets compared to all other experimented models. Additionally, it outperforms the other models in classification results as well as time. © 2022 Tech Science Press. All rights reserved.