Open AccessCoviDecode : Detection of COVID-19 from Chest X-Ray images using Convolutional Neural Networks
Author(s) -
Rishabh Raj
Publication year - 2020
Publication title -
international journal of modern trends in science and technology
Language(s) - English
Resource type - Journals
ISSN - 2455-3778
DOI - 10.46501/ijmtst061283
Subject(s) - convolutional neural network , covid-19 , deep learning , artificial intelligence , computer science , artificial neural network , pattern recognition (psychology) , machine learning , medicine , virology , pathology , disease , outbreak , infectious disease (medical specialty)
ommand, product recommendation and medical diagnosis. The detection of severe acute respiratorysyndrome corona virus 2 (SARS CoV-2), which is responsible for corona virus disease 2019 (COVID-19),using chest X-ray images has life-saving importance for bothpatients and doctors. In addition, in countriesthat are unable to purchase laboratory kits for testing, this becomes even more vital. In this study, we aimedto present the use of deep learning for the high-accuracy detection of COVID-19 using chest X-ray images.Publicly available X-ray images were used in the experiments, which involved the training of deep learningand machine learning classifiers. Experiments were performed using convolutional neural networks andmachine learning models. Images and statistical data were considered separately in the experiments toevaluate the performances of models, and eightfold cross-validation was used. A mean accuracy of 98.50%.A convolutional neural network without pre-processing and with minimized layers is capable of detectingCOVID- 19 in a limited number of, and in imbalanced, chest X-rayimages.