A Hierarchical Classification System for the Detection of Covid-19 from Chest X-Ray Images

With the ever-increasing cases of the Covid-19 pandemic, it is important to leverage deep learning methods to create tools that can aid in relieving the pressure that is put on the limited resources in most developing countries. In this work, we propose a hierarchical classification system for the classification of Covid-19 from Chest X-Ray (CXR) images following a recent proposal of massive use of this modality instead of CT. The system composed of multiple binary classifiers outperforms a tailor-made multi-class classifier COVID-Net. We also show that using well-known established deep learning frameworks combined with a global attention mechanism outperforms the baseline COVID-Net specifically designed for the classification of Covid-19 from CXR images. Our method shows approximately a 4% improvement in the sensitivity to Covid-19 detection from 91% of COVID-Net to 96%. Using popular networks with the possibility of cross-domain transfer learning ensures that the designing and training times are reduced. Furthermore, well-established frameworks can be faster adapted into an application in clinical practice.

[1]  W. Zeng,et al.  Chest CT Findings in Patients With Coronavirus Disease 2019 and Its Relationship With Clinical Features , 2020, Investigative radiology.

[2]  Jenny Benois-Pineau,et al.  Improving Alzheimer's stage categorization with Convolutional Neural Network using transfer learning and different magnetic resonance imaging modalities , 2020, Heliyon.

[3]  Jenny Benois-Pineau,et al.  FuseMe: Classification of sMRI images by fusion of Deep CNNs in 2D+ε projections , 2017, CBMI.

[4]  Enhua Wu,et al.  Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Ruoyu Li,et al.  Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rays , 2018, BCB.

[6]  U. Rajendra Acharya,et al.  Automated detection of COVID-19 cases using deep neural networks with X-ray images , 2020, Computers in Biology and Medicine.

[7]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Simone Busoni,et al.  Chest X-ray in new Coronavirus Disease 2019 (COVID-19) infection: findings and correlation with clinical outcome , 2020, La radiologia medica.

[9]  D. Oliva,et al.  COVID-19 X-ray images classification based on enhanced fractional-order cuckoo search optimizer using heavy-tailed distributions , 2020, Applied Soft Computing.

[10]  Yifan Yu,et al.  CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison , 2019, AAAI.

[11]  Joan Bruna,et al.  Intriguing properties of neural networks , 2013, ICLR.

[12]  A. Osman,et al.  SOM-LWL method for identification of COVID-19 on chest X-rays , 2021, PloS one.

[13]  G. Gao,et al.  A Novel Coronavirus from Patients with Pneumonia in China, 2019 , 2020, The New England journal of medicine.

[14]  Y. Hu,et al.  Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China , 2020, The Lancet.

[15]  T. Pham.  Classification of COVID-19 chest X-rays with deep learning: new models or fine tuning? , 2020, Health Information Science and Systems.

[16]  Ronald M. Summers,et al.  ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases , 2019, Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics.

[17]  Chokri Ben Amar,et al.  Prediction of visual attention with deep CNN on artificially degraded videos for studies of attention of patients with Dementia , 2017, Multimedia Tools and Applications.

[18]  R. Lu,et al.  Detection of SARS-CoV-2 in Different Types of Clinical Specimens. , 2020, JAMA.

[19]  Joseph Paul Cohen,et al.  COVID-19 Image Data Collection: Prospective Predictions Are the Future , 2020, ArXiv.

[20]  Francesco Sardanelli,et al.  Diagnostic Performance of Chest X-Ray for COVID-19 Pneumonia During the SARS-CoV-2 Pandemic in Lombardy, Italy , 2020, Journal of thoracic imaging.

[21]  Ioannis D. Apostolopoulos,et al.  Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases , 2020, Journal of Medical and Biological Engineering.

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

[23]  N. Karimi,et al.  Detection of COVID-19 in X-ray images by classification of bag of visual words using neural networks , 2021, Biomedical Signal Processing and Control.

[24]  Zhiyong Lu,et al.  Automated abnormality classification of chest radiographs using deep convolutional neural networks , 2020, npj Digital Medicine.

[25]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[26]  Shihua Zhao,et al.  The role of imaging in 2019 novel coronavirus pneumonia (COVID-19) , 2020, European Radiology.

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

[28]  Milan Sonka,et al.  Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning , 2020, Medical Image Analysis.

[29]  E. Cavigli,et al.  Chest X-ray in the emergency department during COVID-19 pandemic descending phase in Italy: correlation with patients’ outcome , 2021, La radiologia medica.

[30]  Xin Zhao,et al.  Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches , 2020, Journal of X-ray science and technology.