Journal of radiological science and technology (대한방사선기술학회지:방사선기술과학)

Korean Society of Radiological Science (대한방사선과학회)
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Evaluation of Deep-Learning Feature Based COVID-19 Classifier in Various Neural Network

코로나바이러스 감염증19 데이터베이스에 기반을 둔 인공신경망 모델의 특성 평가

  • Hong, Jun-Yong (Department of Multidisciplinary Radiological Science, Dongseo University) ;
  • Jung, Young-Jin (Department of Multidisciplinary Radiological Science, Dongseo University)
  • 홍준용 (동서대학교 융합방사선학과) ;
  • 정영진 (동서대학교 융합방사선학과)
  • Received : 2020.10.16
  • Accepted : 2020.10.28
  • Published : 2020.10.31
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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

References

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