About: Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers

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Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers
Creator	Kumar, Rahul Arora, Ridhi Bansal, Vipul Buckchash, Himanshu Imran, Javed Narayanan, Narayanan Pandian, Ganesh Raman, Balasubramanian Sahayasheela, Vinodh
Source	MedRxiv
abstract	According to the World Health Organization (WHO), the coronavirus (COVID-19) pandemic is putting even the best healthcare systems across the world under tremendous pressure. The early detection of this type of virus will help in relieving the pressure of the healthcare systems. Chest X-rays has been playing a crucial role in the diagnosis of diseases like Pneumonia. As COVID-19 is a type of influenza, it is possible to diagnose using this imaging technique. With rapid development in the area of Machine Learning (ML) and Deep learning, there had been intelligent systems to classify between Pneumonia and Normal patients. This paper proposes the machine learning-based classification of the extracted deep feature using ResNet152 with COVID-19 and Pneumonia patients on chest X-ray images. SMOTE is used for balancing the imbalanced data points of COVID-19 and Normal patients. This non-invasive and early prediction of novel coronavirus (COVID-19) by analyzing chest X-rays can further be used to predict the spread of the virus in asymptomatic patients. The model is achieving an accuracy of 0.973 on Random Forest and 0.977 using XGBoost predictive classifiers. The establishment of such an approach will be useful to predict the outbreak early, which in turn can aid to control it effectively.
has issue date	2020-04-17(xsd:dateTime)
bibo:doi	10.1101/2020.04.13.20063461
has license	medrxiv
sha1sum (hex)	5da3f835dd8a4e112a76d21db78a85920453478a
schema:url	https://doi.org/10.1101/2020.04.13.20063461
resource representing a document's title	Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers
resource representing a document's body	covid:5da3f835dd8a4e112a76d21db78a85920453478a#body_text
is schema:about of	named entity 'predict' named entity 'Normal' named entity 'Pneumonia' named entity 'patients' named entity 'deep' named entity 'Prediction' named entity '977' named entity 'PLAYING' named entity 'DISEASES' named entity 'IMAGING TECHNIQUE' named entity 'PNEUMONIA' named entity 'HELP' named entity 'PREDICT' named entity 'MACHINE LEARNING' named entity 'ANALYZING' named entity 'PRESSURE' named entity 'EXTRACTED' named entity 'NOVEL CORONAVIRUS' named entity 'BALANCING' covid:arg/5da3f835dd8a4e112a76d21db78a85920453478a named entity 'Random Forest' named entity 'early' named entity 'Pneumonia' named entity 'effectively' named entity 'images' named entity 'Pneumonia' named entity 'predict' named entity 'virus' named entity 'control' named entity 'Images' named entity 'COVID-19' named entity 'virus' named entity 'Machine Learning' named entity 'Pneumonia' named entity 'COVID-19' named entity 'asymptomatic' named entity 'influenza' named entity 'type' named entity 'X-ray images' named entity 'network architecture' named entity 'X-rays' named entity 'CNN' named entity 'Pneumonia' named entity 'AUC' named entity 'COVID' named entity 'deep learning' named entity 'pneumonia' named entity 'COVID-19' named entity 'Convolution' named entity 'COVID' named entity 'confusion matrix' named entity 'ResNet' named entity 'infectious disease' named entity 'shortness of breath' named entity 'COVID' named entity 'machine learning' named entity 'COVID-19' named entity 'preprint' named entity 'India' named entity 'Pneumonia' named entity 'chest X-ray' named entity 'Naive Bayes' named entity 'CT images' named entity 'deep convolutional neural network' named entity 'XGBoost' named entity 'COVID' named entity 'scientific methods' named entity 'confusion matrix' named entity 'X-ray' named entity 'COVID' named entity 'machine learning' named entity 'Random Forest' named entity 'Pneumonia' named entity 'programming language' named entity 'lockdown in India' named entity 'WHO' named entity 'AdaBoost'

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