Transfer learning to detect COVID-19 automatically from X-ray images, using convolutional neural networks

Novel coronavirus pneumonia (COVID-19) is a contagious disease that has already caused thousands of deaths and infected millions of people worldwide. Thus, all technological gadgets that allow the fast detection of COVID- 19 infection with high accuracy can offer help to healthcare professionals. This study is purposed to explore the effectiveness of artificial intelligence (AI) in the rapid and reliable detection of COVID-19 based on chest X-ray imaging. In this study, reliable pre-trained deep learning algorithms were applied to achieve the automatic detection of COVID-19-induced pneumonia from digital chest X-ray images. Moreover, the study aims to evaluate the performance of advanced neural architectures proposed for the classification of medical images over recent years. The data set used in the experiments involves 274 COVID-19 cases, 380 viral pneumonia, and 380 healthy cases, which was derived from several open sources of X-Rays, and the data available online. The confusion matrix provided a basis for testing the post-classification model. Furthermore, an open-source library PYCM was used to support the statistical parameters. The study revealed the superiority of Model vgg16 over other models applied to conduct this research where the model performed best in terms of overall scores and based-class scores. According to the research results, deep Learning with X-ray imaging is useful in the collection of critical biological markers associated with COVID-19 infection. The technique is conducive for the physicians to make a diagnosis of COVID-19 infection. Meanwhile, the high accuracy of this computer-aided diagnostic tool can significantly improve the speed and accuracy of COVID-19 diagnosis.

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

[2]  B. Matthews.  Comparison of the predicted and observed secondary structure of T4 phage lysozyme. , 1975, Biochimica et biophysica acta.

[3]  Deniz Korkmaz,et al.  COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images , 2020, Medical Hypotheses.

[4]  Jan Gorodkin,et al.  Comparing two K-category assignments by a K-category correlation coefficient , 2004, Comput. Biol. Chem..

[5]  Nathalie Japkowicz,et al.  The class imbalance problem: A systematic study , 2002, Intell. Data Anal..

[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]  Asif Iqbal Khan,et al.  CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images , 2020, Computer Methods and Programs in Biomedicine.

[8]  Talha Burak Alakus,et al.  Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks , 2020, Chaos, Solitons & Fractals.

[9]  Victor M Corman,et al.  Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR , 2020, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[10]  Prabira Kumar Sethy,et al.  Detection of Coronavirus Disease (COVID-19) Based on Deep Features , 2020 .

[11]  Chongsheng Zhang,et al.  An empirical comparison on state-of-the-art multi-class imbalance learning algorithms and a new diversified ensemble learning scheme , 2018, Knowl. Based Syst..

[12]  Nitish Srivastava,et al.  Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.

[13]  Farnoosh Naderkhani,et al.  COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images , 2020, Pattern Recognition Letters.

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

[15]  Zhidong Deng,et al.  Recent progress in semantic image segmentation , 2018, Artificial Intelligence Review.

[16]  Saleh Albahli,et al.  A Deep Neural Network to Distinguish COVID-19 from other Chest Diseases using X-ray Images. , 2020, Current medical imaging.

[17]  Xin Yao,et al.  Ensemble of Classifiers Based on Multiobjective Genetic Sampling for Imbalanced Data , 2020, IEEE Transactions on Knowledge and Data Engineering.

[18]  Mesut Toğaçar,et al.  Application of breast cancer diagnosis based on a combination of convolutional neural networks, ridge regression and linear discriminant analysis using invasive breast cancer images processed with autoencoders. , 2019, Medical hypotheses.

[19]  Alexander Wong,et al.  COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images , 2020, Scientific reports.

[20]  Geoffrey I. Webb,et al.  Encyclopedia of Machine Learning , 2011, Encyclopedia of Machine Learning.

[21]  Amit Kumar Jaiswal,et al.  Identifying pneumonia in chest X-rays: A deep learning approach , 2019, Measurement.

[22]  Ryuei Nishii,et al.  Accuracy and inaccuracy assessments in land-cover classification , 1999, IEEE Trans. Geosci. Remote. Sens..

[23]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[24]  Sepand Haghighi,et al.  PyCM: Multiclass confusion matrix library in Python , 2018, J. Open Source Softw..

[25]  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.

[26]  Stephen Kwek,et al.  Applying Support Vector Machines to Imbalanced Datasets , 2004, ECML.

[27]  Juri Opitz,et al.  Macro F1 and Macro F1 , 2019, ArXiv.

[28]  Joachim M. Buhmann,et al.  Bayesian mixed-effects inference on classification performance in hierarchical data sets , 2012, J. Mach. Learn. Res..

[29]  JapkowiczNathalie,et al.  The class imbalance problem: A systematic study , 2002 .

[30]  Abdul Hafeez,et al.  COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs , 2020, ArXiv.

[31]  Sean M. McNee,et al.  Being accurate is not enough: how accuracy metrics have hurt recommender systems , 2006, CHI Extended Abstracts.

[32]  Jun Liu,et al.  Chest CT for Typical 2019-nCoV Pneumonia: Relationship to Negative RT-PCR Testing , 2020, Radiology.

[33]  Douglas M. Hawkins,et al.  The Problem of Overfitting , 2004, J. Chem. Inf. Model..

[34]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[35]  José A. Castellanos,et al.  Probabilistic Performance Evaluation for Multiclass Classification Using the Posterior Balanced Accuracy , 2013, ROBOT.

[36]  Xin Li,et al.  COVID-Xpert: An AI Powered Population Screening of COVID-19 Cases Using Chest Radiography Images , 2020 .

[37]  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.

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