코로나바이러스 감염증19 데이터베이스에 기반을 둔 인공신경망 모델의 특성 평가

• 영문명: Evaluation of Deep-Learning Feature Based COVID-19 Classifier in Various Neural Network
• 발행기관: 대한방사선과학회(구 대한방사선기술학회)
• 저자명: 홍준용(Jun-Yong Hong) 정영진(Young-Jin Jung)
• 간행물 정보: 『방사선기술과학』방사선기술과학 제43권 제5호, 397~404쪽, 전체 8쪽
• 주제분류: 의약학 > 방사선과학
• 파일형태: PDF
• 발행일자: 2020.10.30

영문 초록

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.

목차

Ⅰ. 서 론

Ⅱ. 대상 및 방법

Ⅲ. 결 과

Ⅳ. 고 찰

Ⅴ. 결 론

REFERENCES

키워드

알렉스넷 흉부 방사선검사 코로나바이러스감염증19 심층학습 인공신경망 AlexNet Chest Radiography(CXR) COVID-19 Deep-learning Neural network