A deep convolutional neural network for COVID-19 detection using chest X-rays

Pedro R. A. S. Bassi1, Romis Attux1
1Department of Computer Engineering and Industrial Automation, School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil

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Alber M, Lapuschkin S, Seegerer P, Hägele M, Schütt KT, Montavon G, et al. innvestigate neural networks! J Mach Learn Res. 2019;20:1–8.

Bach S, Binder A, Montavon G, Klauschen F, Müller KR, Samek W. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS One. 2015;10:1–46.

Bassi, P.R.A.S., Attux, R. Covid-19 twice transfer dnns. 2020. https://github.com/PedroRASB/COVID-19-Twice-Transfer-DNNs. Accessed 16 Jun 2020.

Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell. 2012;35(8):1798–828.

Cai Q, Liu X, Guo Z. Identifying architectural distortion in mammogram images via a se-densenet model and twice transfer learning. 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). 2018;1–6.

Cohen JP, Morrison P, Dao L. Covid-19 image data collection. arXiv2003.11597. 2020.

Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. ImageNet: a large-scale hierarchical image database. CVPR09. 2009.

Goodfellow I, Bengio Y, Courville A, Bengio Y. Deep learning.volume 1. Cambridge: MIT press; 2016.

Hopkins UJ. Coronavirus Resource Center. 2020. https://coronavirus.jhu.edu/. Accessed 16 Jun 2020.

Huang G, Liu Z, van der Maaten L, Weinberger KQ. Densely connected convolutional networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016;2261–2269.

Irvin J, Rajpurkar P, Ko M, Yu Y, Ciurea-Ilcus S, Chute C, et al. Chexpert: a large chest radiograph dataset with uncertainty labels and expert comparison: arXiv:1901.07031; 2019.

Jaeger S, Candemir S, Antani S, Wang J, Lu PX, Thoma G. Two public chest x-ray datasets for computer-aided screening of pulmonary diseases. Quant Imaging Med Surg. 2014;4:475–7. https://doi.org/10.3978/j.issn.2223-4292.2014.11.20.

Malivenko G. Pytorch2keras. 2018. https://github.com/nerox8664/pytorch2keras. Accessed 16 Jun 2020.

Montavon G, Binder A, Lapuschkin S, Samek W, Müller, Klaus-Robert EW, et al. Layer-wise relevance propagation: an overview. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning: Springer International Publishing; 2019. p. 193–209.

Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, et al. Chexnet: radiologist-level pneumonia detection on chest x-rays with deep learning: CoRR abs/1711.05225; 2017.

Shoeibi A, Khodatars M, Alizadehsani R, Ghassemi N, Jafari M, Moridian P, et al. Automated detection and forecasting of covid-19 using deep learning techniques: a review: ArXiv abs/2007.10785; 2020.

Thomas AW, Heekeren HR, Müller KR, Samek W. Analyzing neuroimaging data through recurrent deep learning models. Front Neurosci. 2019;13:1321.

Trunk GV. A problem of dimensionality: a simple example. IEEE Trans Pattern Anal Mach Intell. 1979;1:306–7.

Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. Chestx-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. IEEE Conf Comput Vis Pattern Recogn. 2017;2017:3462–71.

Wang L, Wong A. Covid-net: a tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images: arXiv:2003.09871; 2020.

Wang W, Xu Y, Gao R, Lu R, Han K, Wu G. Detection of sars-cov-2 in different types of clinical specimens. JAMA. 2020.

Yang Y, Tresp V, Wunderle M, Fasching PA. Explaining therapypredictions with layer-wise relevance propagation in neural networks. IEEE Int Conf Healthc Inform. 2018;2018:152–62.

Zech J. Reproduce-chexnet. 2018. https://github.com/jrzech/reproduce-chexnet. Accessed 16 Jun 2020.