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http://dx.doi.org/10.17946/JRST.2020.43.5.397

Evaluation of Deep-Learning Feature Based COVID-19 Classifier in Various Neural Network  

Hong, Jun-Yong (Department of Multidisciplinary Radiological Science, Dongseo University)
Jung, Young-Jin (Department of Multidisciplinary Radiological Science, Dongseo University)
Publication Information
Journal of radiological science and technology / v.43, no.5, 2020 , pp. 397-404 More about this Journal
Abstract
Coronavirus disease(COVID-19) is highly infectious disease that directly affects the lungs. To observe the clinical findings from these lungs, the Chest Radiography(CXR) can be used in a fast manner. However, the diagnostic performance via CXR needs to be improved, since the identifying these findings are highly time-consuming and prone to human error. Therefore, Artificial Intelligence(AI) based tool may be useful to aid the diagnosis of COVID-19 via CXR. In this study, we explored various Deep learning(DL) approach to classify COVID-19, other viral pneumonia and normal. For the original dataset and lung-segmented dataset, the pre-trained AlexNet, SqueezeNet, ResNet18, DenseNet201 were transfer-trained and validated for 3 class - COVID-19, viral pneumonia, normal. In the results, AlexNet showed the highest mean accuracy of 99.15±2.69% and fastest training time of 1.61±0.56 min among 4 pre-trained neural networks. In this study, we demonstrated the performance of 4 pre-trained neural networks in COVID-19 diagnosis with CXR images. Further, we plotted the class activation map(CAM) of each network and demonstrated that the lung-segmentation pre-processing improve the performance of COVID-19 classifier with CXR images by excluding background features.
Keywords
AlexNet; Chest Radiography(CXR); COVID-19; Deep-learning; Neural network;
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Times Cited By KSCI : 4  (Citation Analysis)
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