Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods

This study aimed to develop and validate computer-aided diagnosis (CXDx) system for classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray (CXR) images. From two public datasets, 1248 CXR images were obtained, which included 215, 533, and 500 CXR images of COVID-19 pneumonia patients, non-COVID-19 pneumonia patients, and the healthy samples, respectively. The proposed CADx system utilized VGG16 as a pre-trained model and combination of conventional method and mixup as data augmentation methods. Other types of pre-trained models were compared with the VGG16-based model. Single type or no data augmentation methods were also evaluated. Splitting of training/validation/test sets was used when building and evaluating the CADx system. Three-category accuracy was evaluated for test set with 125 CXR images. The three-category accuracy of the CAD system was 83.6% between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy. Sensitivity for COVID-19 pneumonia was more than 90%. The combination of conventional method and mixup was more useful than single type or no data augmentation method. In conclusion, this study was able to create an accurate CADx system for the 3-category classification. Source code of our CADx system is available as open source for COVID-19 research.

[1]  Sasank Chilamkurthy,et al.  Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study , 2018, The Lancet.

[2]  Joseph Paul Cohen,et al.  COVID-19 Image Data Collection , 2020, ArXiv.

[3]  Mizuho Nishio,et al.  Bone segmentation on whole-body CT using convolutional neural network with novel data augmentation techniques , 2020, Comput. Biol. Medicine.

[4]  M. Kuo,et al.  Frequency and Distribution of Chest Radiographic Findings in COVID-19 Positive Patients , 2019, Radiology.

[5]  T. Egglin,et al.  Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT , 2020, Radiology.

[6]  Takashi Matsubara,et al.  Data Augmentation Using Random Image Cropping and Patching for Deep CNNs , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

[7]  Hongyi Zhang,et al.  mixup: Beyond Empirical Risk Minimization , 2017, ICLR.

[8]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

[9]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[11]  Quoc V. Le,et al.  EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.

[12]  A. Sodickson,et al.  Recurrent CT, cumulative radiation exposure, and associated radiation-induced cancer risks from CT of adults. , 2009, Radiology.

[13]  Yoshua Bengio,et al.  Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..

[14]  Andrew Y. Ng,et al.  CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning , 2017, ArXiv.

[15]  Yicheng Fang,et al.  Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR , 2020, Radiology.

[16]  C. Campèse,et al.  First cases of coronavirus disease 2019 (COVID-19) in France: surveillance, investigations and control measures, January 2020 , 2020, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[17]  Tomohiro Kuroda,et al.  Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning , 2018, PloS one.

[18]  Chao Lu,et al.  Retrospective study , 2016, Medicine.

[19]  Ömer ACER,et al.  Comparison of RT-PCR , 2020 .

[20]  Richard K. G. Do,et al.  Convolutional neural networks: an overview and application in radiology , 2018, Insights into Imaging.

[21]  Chao Yang,et al.  A Survey on Deep Transfer Learning , 2018, ICANN.

[22]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).