Machine learning applications for COVID-19 outbreak management

Abstract

Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in many applications, including detecting and monitoring patients, notably in COVID-19 management. Different medical imaging systems, such as computed tomography (CT) and X-ray, offer ML an excellent platform for combating the pandemic. Because of this need, a significant quantity of study has been carried out; thus, in this work, we employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers. Imaging methods, survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps are the seven key uses of applications employed in the COVID-19 pandemic. Conventional neural networks (CNNs), long short-term memory networks (LSTM), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, random forest, and other ML techniques are frequently used in such scenarios. Next, cutting-edge applications related to ML techniques for pandemic medical issues are discussed. Various problems and challenges linked with ML applications for this pandemic were reviewed. It is expected that additional research will be conducted in the upcoming to limit the spread and catastrophe management. According to the data, most papers are evaluated mainly on characteristics such as flexibility and accuracy, while other factors such as safety are overlooked. Also, Keras was the most often used library in the research studied, accounting for 24.4 percent of the time. Furthermore, medical imaging systems are employed for diagnostic reasons in 20.4 percent of applications.

Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan Images

“…To date, a wide range of DL models have been proposed to solve various problems (both guided learning and individual learning) [ 34 ]. Most of them are used for purposes such as image ordering, object recognition, and general language preparation, and the output layer consists of several nodes that determine the probability of each label.…”

“…According to [ 3 ], the authors utilized a combined CNN-LTSM model using a time-series dataset to predict the confirmed cases of COVID-19 [ 33 ]. The CNN-LTSM encoder-decoder technique helps significantly boost prediction performance [ 34 ]. The study in [ 4 ] proposed an RNN-based model which was the modified version of LSTM to predict the mortality ratio, infected patients, and recovered positive patients.…”

SARS-CoV-2 virus classification based on stacked sparse autoencoder

“…Many works from the state of the art are using deep learning to solve biomedical problems [23] , [24] , [25] , [26] , [27] . Recently works in literature have been applying deep learning as tertiary analysis such as viral prediction, viral host prediction, and viral segments prediction [17] , [12] , [18] , [20] , [13] , [28] , [19] , [29] , [14] , [30] , [31] , [32] , [15] , [16] , [33] , [34] , [35] , [36] .…”

Medical Ultrasound Image Segmentation With Deep Learning Models

“…When ultrasound imaging detects patient lesions, the smoothness of the organ surface and the location of the lesions can be checked, and real-time image inspection can be performed [15]. Deep learning technology has achieved success in the construction of large-scale image datasets for natural image recognition [16]. The bilinear convolutional neural network (BCNN) is able to classify images very well.…”