Classifying COVID-19 positive X-ray using deep learning models

COVID-19 is a pandemic characterized by uncertainty not only in transmission and pathogenicity, but also in disease-specific control options. Despite many governmental measures, the disease is spreading and in many countries, the public health system is close to be collapsed. Alternative techniques should be taken in order to minimize the COVID-19 negative impacts on the society. This work presents preliminary results of deep learning models to classify COVID-19 positive based on X-ray images. We provide binary classification (COVID-19 vs healhty, and COVID-19 vs pneumonia) and also multiclass (COVID-19 vs pneumonia vs healhty). Results show that VGG models present the best results, achiving 98.81% of precision in binary classification, and 91.68% in multiclass classification.

[1]  Sergey Levine,et al.  Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search , 2015, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[2]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

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

[4]  Wellington Pinheiro dos Santos,et al.  Model Based on Deep Feature Extraction for Diagnosis of Alzheimer’s Disease , 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

[5]  Xiang Xie,et al.  COVID-19 and the cardiovascular system , 2020, Nature Reviews Cardiology.

[6]  Ali Narin,et al.  Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks , 2020, Pattern Analysis and Applications.

[7]  Chris Metz,et al.  COLAP: A predictive framework for service function chain placement in a multi-cloud environment , 2017, 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC).

[8]  P. Lakhani,et al.  Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. , 2017, Radiology.

[9]  Ioannis D. Apostolopoulos,et al.  Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks , 2020, Physical and Engineering Sciences in Medicine.

[10]  J. Hageman The Coronavirus Disease 2019 (COVID-19). , 2020, Pediatric annals.

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

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

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

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

[15]  Hayit Greenspan,et al.  Deep learning with non-medical training used for chest pathology identification , 2015, Medical Imaging.

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

[17]  Ezz El-Din Hemdan,et al.  COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images , 2020, ArXiv.

[18]  Parisa Rashidi,et al.  Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis , 2017, IEEE Journal of Biomedical and Health Informatics.

[19]  Weiguo Fan,et al.  A new image classification method using CNN transfer learning and web data augmentation , 2018, Expert Syst. Appl..

[20]  Benjamin Q. Huynh,et al.  SU-D-207B-06: Predicting Breast Cancer Malignancy On DCE-MRI Data Using Pre-Trained Convolutional Neural Networks. , 2016, Medical physics.

[21]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[22]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[23]  André Machado Siqueira,et al.  Effect of High vs Low Doses of Chloroquine Diphosphate as Adjunctive Therapy for Patients Hospitalized With Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection: A Randomized Clinical Trial. , 2020, JAMA network open.

[24]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Ripul Ghosh,et al.  Deep learning approach for human action recognition in infrared images , 2018, Cognitive Systems Research.

[26]  Xiaogang Li,et al.  Fall detection for elderly person care using convolutional neural networks , 2017, 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI).

[27]  Sahil Shah,et al.  Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques , 2015, Expert Syst. Appl..

[28]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[29]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[30]  Jinbo Song,et al.  Visual guided deep learning scheme for fall detection , 2017, 2017 13th IEEE Conference on Automation Science and Engineering (CASE).

[31]  S. Merler,et al.  Baseline Characteristics and Outcomes of 1591 Patients Infected With SARS-CoV-2 Admitted to ICUs of the Lombardy Region, Italy. , 2020, JAMA.

[32]  D. Falzarano,et al.  SARS and MERS: recent insights into emerging coronaviruses , 2016, Nature Reviews Microbiology.

[33]  Simone Palazzo,et al.  Deep learning for automated skeletal bone age assessment in X‐ray images , 2017, Medical Image Anal..

[34]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[35]  R. Wunderink,et al.  MERS, SARS and other coronaviruses as causes of pneumonia , 2017, Respirology.

[36]  Arun Ross,et al.  On automated source selection for transfer learning in convolutional neural networks , 2018, Pattern Recognit..

[37]  Chunhua Shen,et al.  COVID-19 Screening on Chest X-ray Images Using Deep Learning based Anomaly Detection , 2020, ArXiv.