@Article{cmc.2020.011326,
AUTHOR = {Mohammad Shorfuzzaman, Mehedi Masud},
TITLE = {On the Detection of COVID-19 from Chest X-Ray Images Using  CNN-Based Transfer Learning},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {3},
PAGES = {1359--1381},
URL = {http://www.techscience.com/cmc/v64n3/39434},
ISSN = {1546-2226},
ABSTRACT = {Coronavirus disease (COVID-19) is an extremely infectious disease and 
possibly causes acute respiratory distress or in severe cases may lead to death. There has 
already been some research in dealing with coronavirus using machine learning 
algorithms, but few have presented a truly comprehensive view. In this research, we show 
how convolutional neural network (CNN) can be useful to detect COVID-19 using chest 
X-ray images. We leverage the CNN-based pre-trained models as feature extractors to 
substantiate transfer learning and add our own classifier in detecting COVID-19. In this 
regard, we evaluate performance of five different pre-trained models with fine-tuning the 
weights from some of the top layers. We also develop an ensemble model where the 
predictions from all chosen pre-trained models are combined to generate a single output. 
The models are evaluated through 5-fold cross validation using two publicly available 
data repositories containing healthy and infected (both COVID-19 and other pneumonia) 
chest X-ray images. We also leverage two different visualization techniques to observe 
how efficiently the models extract important features related to the detection of COVID-
19 patients. The models show high degree of accuracy, precision, and sensitivity. We 
believe that the models will aid medical professionals with improved and faster patient 
screening and pave a way to further COVID-19 research.},
DOI = {10.32604/cmc.2020.011326}
}