Evaluating Convolutional Neural Networks for COVID-19 classification in chest X-ray images

• Leonardo Rodrigues UFV
• Larissa Rodrigues UFV
• Danilo da Silva UFV
• João Fernando Mari UFV

DOI: https://doi.org/10.5753/wvc.2020.13480

Resumo

Coronavirus Disease 2019 (COVID-19) pandemic rapidly spread globally, impacting the lives of billions of people. The effective screening of infected patients is a critical step to struggle with COVID-19, and treating the patients avoiding this quickly disease spread. The need for automated and scalable methods has increased due to the unavailability of accurate automated toolkits. Recent researches using chest X-ray images suggest they include relevant information about the COVID-19 virus. Hence, applying machine learning techniques combined with radiological imaging promises to identify this disease accurately. It is straightforward to collect these images once it is spreadly shared and analyzed in the world. This paper presents a method for automatic COVID-19 detection using chest Xray images through four convolutional neural networks, namely: AlexNet, VGG-11, SqueezeNet, and DenseNet-121. This method had been providing accurate diagnostics for positive or negative COVID-19 classification. We validate our experiments using a ten-fold cross-validation procedure over the training and test sets. Our findings include the shallow fine-tuning and data augmentation strategies that can assist in dealing with the low number of positive COVID-19 images publicly available. The accuracy for all CNNs is higher than 97.00%, and the SqueezeNet model achieved the best result with 99.20%.

Palavras-chave: COVID-19, coronavirus, chest X-ray, convolutional neural networks, data augmentation, fine-tuning

Referências

World Health Organization. Coronavirus disease 2019 (covid-19): situation report, 73. Technical documents, 2020-04-02.

Peng Zhou, Xing-Lou Yang, Xian-Guang Wang, Ben Hu, Lei Zhang, Wei Zhang, Hao-Rui Si, Yan Zhu, Bei Li, Chao-Lin Huang, Hui-Dong Chen, Jing Chen, Yun Luo, Hua Guo, Ren-Di Jiang, Mei-Qin Liu, Ying Chen, Xu-Rui Shen, Xi Wang, Xiao-Shuang Zheng, Kai Zhao, Quan-Jiao Chen, Fei Deng, Lin-Lin Liu, Bing Yan, Fa-Xian Zhan, Yan- Yi Wang, Geng-Fu Xiao, and Zheng-Li Shi. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature, 579(7798):270–273, 2020.

Graziano Onder, Giovanni Rezza, and Silvio Brusaferro. Case-Fatality Rate and Characteristics of Patients Dying in Relation to COVID-19 in Italy. JAMA, 03 2020.

Wei-jie Guan, Zheng-yi Ni, Yu Hu, Wen-hua Liang, Chun-quan Ou, Jian-xing He, Lei Liu, Hong Shan, Chun-liang Lei, David S.C. Hui, Bin Du, Lan-juan Li, Guang Zeng, Kwok-Yung Yuen, Ru-chong Chen, Chun-li Tang, Tao Wang, Ping-yan Chen, Jie Xiang, Shi-yue Li, Jin-lin Wang, Zi-jing Liang, Yi-xiang Peng, Li Wei, Yong Liu, Ya-hua Hu, Peng Peng, Jian-ming Wang, Ji-yang Liu, Zhong Chen, Gang Li, Zhijian Zheng, Shao-qin Qiu, Jie Luo, Chang-jiang Ye, Shao-yong Zhu, and Nan-shan Zhong. Clinical characteristics of coronavirus disease 2019 in china. New England Journal of Medicine, 382(18):1708–1720, 2020.

Giacomo Grasselli, Antonio Pesenti, and Maurizio Cecconi. Critical Care Utilization for the COVID-19 Outbreak in Lombardy, Italy: Early Experience and Forecast During an Emergency Response. JAMA, 323(16):1545–1546, 04 2020.

Loris Nanni, Stefano Ghidoni, and Sheryl Brahnam. Ensemble of convolutional neural networks for bioimage classification. Applied Computing and Informatics, 2018.

Daniel S. Kermany, Michael Goldbaum, Wenjia Cai, Carolina C.S. Valentim, Huiying Liang, Sally L. Baxter, Alex McKeown, Ge Yang, Xiaokang Wu, Fangbing Yan, Justin Dong, Made K. Prasadha, Jacqueline Pei, Magdalene Y.L. Ting, Jie Zhu, Christina Li, Sierra Hewett, Jason Dong, Ian Ziyar, Alexander Shi, Runze Zhang, Lianghong Zheng, Rui Hou, William Shi, Xin Fu, Yaou Duan, Viet A.N. Huu, Cindy Wen, Edward D. Zhang, Charlotte L. Zhang, Oulan Li, Xiaobo Wang, Michael A. Singer, Xiaodong Sun, Jie Xu, Ali Tafreshi, M. Anthony Lewis, Huimin Xia, and Kang Zhang. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell, 172(5):1122 – 1131.e9, 2018.

Larissa Ferreira Rodrigues, Murilo Coelho Naldi, and João Fernando Mari. Comparing convolutional neural networks and preprocessing techniques for hep-2 cell classification in immunofluorescence images. Computers in Biology and Medicine, 116:103542, 2020.

Yicheng Fang, Huangqi Zhang, Jicheng Xie, Minjie Lin, Lingjun Ying, Peipei Pang, and Wenbin Ji. Sensitivity of chest ct for covid-19: Comparison to rt-pcr. Radiology, 0(0):200432, 0. PMID: 32073353.

Tao Ai, Zhenlu Yang, Hongyan Hou, Chenao Zhan, Chong Chen, Wenzhi Lv, Qian Tao, Ziyong Sun, and Liming Xia. Correlation of chest ct and rt-pcr testing in coronavirus disease 2019 (covid-19) in china: A report of 1014 cases. Radiology, 0(0):200642, 0. PMID: 32101510.

Geoffrey D. Rubin, Christopher J. Ryerson, Linda B. Haramati, Nicola Sverzellati, Jeffrey P. Kanne, Suhail Raoof, Neil W. Schluger, Annalisa Volpi, Jae-Joon Yim, Ian B.K. Martin, Deverick J. Anderson, Christina Kong, Talissa Altes, Andrew Bush, Sujal R. Desai, Jonathan Goldin, Jin Mo Goo, Marc Humbert, Yoshikazu Inoue, Hans-Ulrich Kauczor, Fengming Luo, Peter J. Mazzone, Mathias Prokop, Martine Remy-Jardin, Luca Richeldi, Cornelia M. Schaefer-Prokop, Noriyuki Tomiyama, Athol U. Wells, and Ann N. Leung. The role of chest imaging in patient management during the covid-19 pandemic: A multinational consensus statement from the fleischner society. Chest, 2020.

Ali Narin, Ceren Kaya, and Ziynet Pamuk. Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks, 2020.

Joseph Paul Cohen, Paul Morrison, and Lan Dao. Covid-19 image data collection. arXiv, 2020.

P Mooney. Chest x-ray images (pneumonia). kaggle, Marzo, 2018.

Ezz El-Din Hemdan, Marwa A. Shouman, and Mohamed Esmail Karar. Covidx-net: A framework of deep learning classifiers to diagnose covid- 19 in x-ray images, 2020.

Prabira Kumar Sethy and Santi Kumari Behera. Detection of coronavirus disease (covid-19) based on deep features. Preprints, 2020030300:2020, 2020.

Linda Wang and Alexander Wong. Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images, 2020.

Ioannis D. Apostolopoulos and Tzani A. Mpesiana. Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine, pages 1–6, Apr 2020. PMC7118364[pmcid].

Asif Iqbal Khan, Junaid Latief Shah, and Mohammad Mudasir Bhat. Coronet: A deep neural network for detection and diagnosis of covid- 19 from chest x-ray images. Computer Methods and Programs in Biomedicine, 196:105581, 2020.

J. Deng, W. Dong, R. Socher, L. J. Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 248–255, June 2009.

Tulin Ozturk, Muhammed Talo, Eylul Azra Yildirim, Ulas Baran Baloglu, Ozal Yildirim, and U. [Rajendra Acharya]. Automated detection of covid-19 cases using deep neural networks with x-ray images. Computers in Biology and Medicine, 121:103792, 2020.

Ferhat Ucar and Deniz Korkmaz. Covidiagnosis-net: Deep bayessqueezenet based diagnosis of the coronavirus disease 2019 (covid-19) from x-ray images. Medical Hypotheses, 140:109761, 2020.

Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally, and Kurt Keutzer. Squeezenet: Alexnet-level accuracy with 50x fewer parameters and ¡0.5mb model size, 2016.

Rodolfo M. Pereira, Diego Bertolini, Lucas O. Teixeira, Carlos N. Silla, and Yandre M.G. Costa. Covid-19 identification in chest x-ray images on flat and hierarchical classification scenarios. Computer Methods and Programs in Biomedicine, 194:105532, 2020.

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105, 2012.

Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org.

M. A. Ponti, L. S. F. Ribeiro, T. S. Nazare, T. Bui, and J. Collomosse. Everything you wanted to know about deep learning for computer vision but were afraid to ask. In 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T), pages 17–41, Oct 2017.

Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556, 2014.

Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700–4708, 2017.

Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift, 2015.

N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall, M. B. Gotway, and J. Liang. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Transactions on Medical Imaging, 35(5):1299–1312, 2016.

Mohammadhassan Izadyyazdanabadi, Evgenii Belykh, Michael Mooney, Nikolay Martirosyan, Jennifer Eschbacher, Peter Nakaji, Mark C. Preul, and Yezhou Yang. Convolutional neural networks: Ensemble modeling, fine-tuning and unsupervised semantic localization for neurosurgical cle images. Journal of Visual Communication and Image Representation, 54:10 – 20, 2018.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324, Nov 1998.

Pierre A. Devijver and Josef Kittler. Pattern Recognition: A Statistical Approach. Prentice-Hall, 1982.

Richard O. Duda, Peter E. Hart, and David G. Stork. Pattern Classification (2Nd Edition). Wiley-Interscience, New York, NY, USA, 2000.

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32, pages 8026–8037. Curran Associates, Inc., 2019.