Evaluation of Scalability and Degree of Fine-Tuning of Deep Convolutional Neural Networks for COVID-19 Screening on Chest X-ray Images Using Explainable Deep-Learning Algorithm.
Evaluation of Scalability and Degree of Fine-Tuning of Deep Convolutional Neural Networks for COVID-19 Screening on Chest X-ray Images Using Explainable Deep-Learning Algorithm.
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DOI：
10.3390/jpm10040213
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发表时间：
2020-11-07
影响因子：
--
通讯作者：
Lee KY
中科院分区：
文献类型：
--
作者：
Lee KS;Kim JY;Jeon ET;Choi WS;Kim NH;Lee KY
According to recent studies, patients with COVID-19 have different feature characteristics on chest X-ray (CXR) than those with other lung diseases. This study aimed at evaluating the layer depths and degree of fine-tuning on transfer learning with a deep convolutional neural network (CNN)-based COVID-19 screening in CXR to identify efficient transfer learning strategies. The CXR images used in this study were collected from publicly available repositories, and the collected images were classified into three classes: COVID-19, pneumonia, and normal. To evaluate the effect of layer depths of the same CNN architecture, CNNs called VGG-16 and VGG-19 were used as backbone networks. Then, each backbone network was trained with different degrees of fine-tuning and comparatively evaluated. The experimental results showed the highest AUC value to be 0.950 concerning COVID-19 classification in the experimental group of a fine-tuned with only 2/5 blocks of the VGG16 backbone network. In conclusion, in the classification of medical images with a limited number of data, a deeper layer depth may not guarantee better results. In addition, even if the same pre-trained CNN architecture is used, an appropriate degree of fine-tuning can help to build an efficient deep learning model.
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DOI：
10.1146/annurev-bioeng-071516-044442
发表时间：
2017-06-21
影响因子：
9.7
作者：
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通讯作者：
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影响因子：
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DOI：
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发表时间：
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期刊：
RADIOLOGY-CARDIOTHORACIC IMAGING
影响因子：
--
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通讯作者：
Agarwal, Prachi P.
影响因子：
5.1
作者：
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