Research on coronavirus disease 2019 (COVID-19) detection method based on depthwise separable DenseNet in chest X-ray images_Journal of Biomedical Engineering

Authors：

FENG Yibo , QIU Dawei , CAO Hui , ZHANG Junzhong , XIN Zaihai ,  LIU Jing

College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250355, P.R.China;

Corresponding author：

LIU Jing, Email: liuj_jn@163.com

Keywords：

convolutional neural network; coronavirus disease 2019; depthwise separable DenseNet; medical image classification

DOI：

10.7507/1001-5515.202005056

Video：

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Coronavirus disease 2019 (COVID-19) has spread rapidly around the world. In order to diagnose COVID-19 more quickly, in this paper, a depthwise separable DenseNet was proposed. The paper constructed a deep learning model with 2 905 chest X-ray images as experimental dataset. In order to enhance the contrast, the contrast limited adaptive histogram equalization (CLAHE) algorithm was used to preprocess the X-ray image before network training, then the images were put into the training network and the parameters of the network were adjusted to the optimal. Meanwhile, Leaky ReLU was selected as the activation function. VGG16, ResNet18, ResNet34, DenseNet121 and SDenseNet models were used to compare with the model proposed in this paper. Compared with ResNet34, the proposed classification model of pneumonia had improved 2.0%, 2.3% and 1.5% in accuracy, sensitivity and specificity respectively. Compared with the SDenseNet network without depthwise separable convolution, number of parameters of the proposed model was reduced by 43.9%, but the classification effect did not decrease. It can be found that the proposed DWSDenseNet has a good classification effect on the COVID-19 chest X-ray images dataset. Under the condition of ensuring the accuracy as much as possible, the depthwise separable convolution can effectively reduce number of parameters of the model.

Citation： FENG Yibo, QIU Dawei, CAO Hui, ZHANG Junzhong, XIN Zaihai, LIU Jing. Research on coronavirus disease 2019 (COVID-19) detection method based on depthwise separable DenseNet in chest X-ray images. Journal of Biomedical Engineering, 2020, 37(4): 557-565. doi: 10.7507/1001-5515.202005056 Copy

1.	Ai Tao, Yang Zhenlu, Hou Hongyan, <italic>et al</italic>. Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: A report of 1014 cases. Radiology, 2020: 200642.
2.	Liang Tingbo. Handbook of COVID-19 prevention and treatment, 2020.
3.	Kanne J P. Chest CT findings in 2019 novel coronavirus (2019-nCoV) infections from Wuhan, China: key points for the radiologist. Radiology, 2020, 295(1): 16-17.
4.	Bullock J, Pham K H, Lam C S N, et al. Mapping the landscape of artificial intelligence applications against COVID-19. arXiv preprint arXiv, 2020: 2003.11336.
5.	梁蒙蒙, 周涛, 张飞飞, 等. 卷积神经网络及其在医学图像分析中的应用研究. 生物医学工程学杂志, 2018, 35(6): 977-985.
6.	韩坤, 潘海为, 张伟, 等. 基于多模态医学图像的 Alzheimer 病分类方法. 清华大学学报: 自然科学版, 2020, 60(8): 664-671.
7.	李端, 张洪欣, 刘知青, 等. 基于深度残差卷积神经网络的心电信号心律不齐识别. 生物医学工程学杂志, 2019, 36(2): 189-198.
8.	黄盛, 李菲菲, 陈虬. 基于改进深度残差网络的计算断层扫描图像分类算法. 光学学报, 2020, 40(3): 56-64.
9.	刘一鸣, 侯智超, 李晓琴, 等. 基于卷积神经网络的肺结节检测方法. 生物医学工程学杂志, 2019, 36(6): 969-977, 985.
10.	Narin A, Kaya C, Pamuk Z. Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. arXiv preprint arXiv, 2020: 2003.10849.
11.	Jin Shuo, Wang Bo, Xu Haibo, et al. AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks. medRxiv, 2020. DOI: 10.1101/2020.03.19.20039354.
12.	Shi Feng, Wang Jun, Shi Jun, et al. Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for covid-19[J]. IEEE Rev Biomed Eng, 2020. DOI: 10.1109/RBME.2020.2987975.
13.	Bernheim A, Mei Xueyan, Huang Mingqian, <italic>et al</italic>. Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection. Radiology, 2020, 295(3): 200463.
14.	孙冬梅, 陆剑锋, 张善卿. 一种改进 CLAHE 算法在医学试纸条图像增强中的应用. 中国生物医学工程学报, 2016, 35(4): 502-506.
15.	张驰名, 王庆凤, 刘志勤, 等. 基于深度迁移学习的肺结节辅助诊断方法. 计算机工程, 2020, 46(1): 271-278.
16.	周飞燕, 金林鹏, 董军. 卷积神经网络研究综述. 计算机学报, 2017, 40(6): 1229-1251.
17.	LeCun Y, Bottou L, Bengio Y, <italic>et al</italic>. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
18.	Lin Min, Chen Qiang, Yan Shuicheng. Network in network. arXiv preprint arXiv, 2013: 1312.4400.
19.	Huang Gao, Liu Zhuang, van der Maaten L, et al. Densely connected convolutional networks// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 4700-4708.
20.	Huang Zhiwen, Zhu Xingxing, Ding Mingyue, <italic>et al</italic>. Medical image classification using a light-weighted hybrid neural network based on PCANet and DenseNet. IEEE Access, 2020, 8: 24697-24712.
21.	Sifre L, Mallat S. Rigid-motion scattering for texture classification. arXiv preprint arXiv, 2014: 1403.1687.
22.	刘媛媛, 张硕, 于海业, 等. 基于语义分割的复杂场景下的秸秆检测. 光学精密工程, 2020, 28(1): 200-211.
23.	Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv, 2014: 1409.1556.
24.	He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Deep residual learning for image recognition// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 770-778.
25.	Chowdhury M E H, Rahman T, Khandakar A, et al. Can AI help in screening viral and COVID-19 pneumonia. arXiv preprint arXiv, 2020: 2003.13145.
26.	Bassi P R A S, Attux R. A deep convolutional neural network for COVID-19 detection using chest X-rays. arXiv preprint arXiv, 2020: 2005.01578.

1. Ai Tao, Yang Zhenlu, Hou Hongyan, <italic>et al</italic>. Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: A report of 1014 cases. Radiology, 2020: 200642.
2. Liang Tingbo. Handbook of COVID-19 prevention and treatment, 2020.
3. Kanne J P. Chest CT findings in 2019 novel coronavirus (2019-nCoV) infections from Wuhan, China: key points for the radiologist. Radiology, 2020, 295(1): 16-17.
4. Bullock J, Pham K H, Lam C S N, et al. Mapping the landscape of artificial intelligence applications against COVID-19. arXiv preprint arXiv, 2020: 2003.11336.
5. 梁蒙蒙, 周涛, 张飞飞, 等. 卷积神经网络及其在医学图像分析中的应用研究. 生物医学工程学杂志, 2018, 35(6): 977-985.
6. 韩坤, 潘海为, 张伟, 等. 基于多模态医学图像的 Alzheimer 病分类方法. 清华大学学报: 自然科学版, 2020, 60(8): 664-671.
7. 李端, 张洪欣, 刘知青, 等. 基于深度残差卷积神经网络的心电信号心律不齐识别. 生物医学工程学杂志, 2019, 36(2): 189-198.
8. 黄盛, 李菲菲, 陈虬. 基于改进深度残差网络的计算断层扫描图像分类算法. 光学学报, 2020, 40(3): 56-64.
9. 刘一鸣, 侯智超, 李晓琴, 等. 基于卷积神经网络的肺结节检测方法. 生物医学工程学杂志, 2019, 36(6): 969-977, 985.
10. Narin A, Kaya C, Pamuk Z. Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. arXiv preprint arXiv, 2020: 2003.10849.
11. Jin Shuo, Wang Bo, Xu Haibo, et al. AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks. medRxiv, 2020. DOI: 10.1101/2020.03.19.20039354.
12. Shi Feng, Wang Jun, Shi Jun, et al. Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for covid-19[J]. IEEE Rev Biomed Eng, 2020. DOI: 10.1109/RBME.2020.2987975.
13. Bernheim A, Mei Xueyan, Huang Mingqian, <italic>et al</italic>. Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection. Radiology, 2020, 295(3): 200463.
14. 孙冬梅, 陆剑锋, 张善卿. 一种改进 CLAHE 算法在医学试纸条图像增强中的应用. 中国生物医学工程学报, 2016, 35(4): 502-506.
15. 张驰名, 王庆凤, 刘志勤, 等. 基于深度迁移学习的肺结节辅助诊断方法. 计算机工程, 2020, 46(1): 271-278.
16. 周飞燕, 金林鹏, 董军. 卷积神经网络研究综述. 计算机学报, 2017, 40(6): 1229-1251.
17. LeCun Y, Bottou L, Bengio Y, <italic>et al</italic>. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
18. Lin Min, Chen Qiang, Yan Shuicheng. Network in network. arXiv preprint arXiv, 2013: 1312.4400.
19. Huang Gao, Liu Zhuang, van der Maaten L, et al. Densely connected convolutional networks// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 4700-4708.
20. Huang Zhiwen, Zhu Xingxing, Ding Mingyue, <italic>et al</italic>. Medical image classification using a light-weighted hybrid neural network based on PCANet and DenseNet. IEEE Access, 2020, 8: 24697-24712.
21. Sifre L, Mallat S. Rigid-motion scattering for texture classification. arXiv preprint arXiv, 2014: 1403.1687.
22. 刘媛媛, 张硕, 于海业, 等. 基于语义分割的复杂场景下的秸秆检测. 光学精密工程, 2020, 28(1): 200-211.
23. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv, 2014: 1409.1556.
24. He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Deep residual learning for image recognition// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 770-778.
25. Chowdhury M E H, Rahman T, Khandakar A, et al. Can AI help in screening viral and COVID-19 pneumonia. arXiv preprint arXiv, 2020: 2003.13145.
26. Bassi P R A S, Attux R. A deep convolutional neural network for COVID-19 detection using chest X-rays. arXiv preprint arXiv, 2020: 2005.01578.

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