Detecting COVID-19 in chest images based on deep transfer learning and machine learning algorithms

This study aimed to propose an automatic prediction of COVID-19 disease using chest CT images based on deep transfer learning models and machine learning (ML) algorithms. The dataset consisted of 5480 samples in two classes, including 2740 CT chest images of patients with confirmed COVID-19 and 2740 images of suspected cases was assessed. The DenseNet201 model has obtained the highest training with an accuracy of 100%. In combining pre-trained models with ML algorithms, the DenseNet201 model and KNN algorithm have received the best performance with an accuracy of 100%. Created map by t-SNE in the DenseNet201 model showed not any points clustered with the wrong class. The mentioned models can be used in remote places, in low- and middle-income countries, and laboratory equipment with limited resources to overcome a shortage of radiologists.

[1]  Yuedong Yang,et al.  Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[2]  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.

[3]  Zenghui Wang,et al.  Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review , 2017, Neural Computation.

[4]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[5]  Fei Wang,et al.  Deep learning for healthcare: review, opportunities and challenges , 2018, Briefings Bioinform..

[6]  Alfonso J. Rodriguez-Morales,et al.  Clinical, laboratory and imaging features of COVID-19: A systematic review and meta-analysis , 2020, Travel Medicine and Infectious Disease.

[7]  Abdulkadir Şengür,et al.  Deep learning approaches for COVID-19 detection based on chest X-ray images , 2020, Expert Systems with Applications.

[8]  Vladimir Vapnik,et al.  Support-vector networks , 2004, Machine Learning.

[9]  Jun Liu,et al.  Relation Between Chest CT Findings and Clinical Conditions of Coronavirus Disease (COVID-19) Pneumonia: A Multicenter Study. , 2020, AJR. American journal of roentgenology.

[10]  Yizhou Yu,et al.  A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images , 2020, European Radiology.

[11]  W. Ko,et al.  Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges , 2020, International Journal of Antimicrobial Agents.

[12]  Shengcai Liao,et al.  Soft-Margin Softmax for Deep Classification , 2017, ICONIP.

[13]  R nbspPatelBrijain,et al.  A Survey on Decision Tree Algorithm for Classification , 2014 .

[14]  L. Xia,et al.  Coronavirus Disease 2019 (COVID-19): Role of Chest CT in Diagnosis and Management. , 2020, AJR. American journal of roentgenology.

[15]  Neena Aloysius,et al.  A review on deep convolutional neural networks , 2017, 2017 International Conference on Communication and Signal Processing (ICCSP).

[16]  Mohammadreza Ghorvei,et al.  A machine learning method based on lesion segmentation for quantitative analysis of CT radiomics to detect COVID-19 , 2020, 2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS).

[17]  C. Macintyre.  Case isolation, contact tracing, and physical distancing are pillars of COVID-19 pandemic control, not optional choices , 2020, The Lancet Infectious Diseases.

[18]  C. Y. Peng,et al.  An Introduction to Logistic Regression Analysis and Reporting , 2002 .

[19]  J. Ross Quinlan,et al.  Simplifying Decision Trees , 1987, Int. J. Man Mach. Stud..

[20]  William Stafford Noble,et al.  Support vector machine , 2013 .

[21]  Maruf Pasha,et al.  Survey of Machine Learning Algorithms for Disease Diagnostic , 2017 .

[22]  Pavel Král,et al.  Deep Generalized Max Pooling , 2019, 2019 International Conference on Document Analysis and Recognition (ICDAR).

[23]  Manjit Kaur,et al.  Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays , 2020, IRBM.

[24]  Yabo Fu,et al.  Deep Learning in Medical Image Registration: A Review , 2020, Physics in medicine and biology.

[25]  Burhan Ergen,et al.  A Deep Feature Learning Model for Pneumonia Detection Applying a Combination of mRMR Feature Selection and Machine Learning Models , 2020, IRBM.

[26]  L. Abel,et al.  Genetic epidemiology of infectious diseases in humans: design of population-based studies. , 1998, Emerging infectious diseases.

[27]  Q. Tao,et al.  Serial Quantitative Chest CT Assessment of COVID-19: A Deep Learning Approach , 2020, Radiology. Cardiothoracic imaging.

[28]  Yaozong Gao,et al.  Lung Infection Quantification of COVID-19 in CT Images with Deep Learning , 2020, ArXiv.

[29]  Shi Qiu,et al.  The ensemble deep learning model for novel COVID-19 on CT images , 2020, Applied Soft Computing.

[30]  Y. Guan,et al.  Early diagnosis of SARS Coronavirus infection by real time RT-PCR , 2003, Journal of Clinical Virology.

[31]  Asifullah Khan,et al.  A survey of the recent architectures of deep convolutional neural networks , 2019, Artificial Intelligence Review.

[32]  Mesut Toğaçar,et al.  COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches , 2020, Computers in Biology and Medicine.

[33]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  Samuel H. Hawkins,et al.  Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among Patients with Lung Adenocarcinoma , 2016, Tomography.

[35]  James M. Keller,et al.  A fuzzy K-nearest neighbor algorithm , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[36]  R. Pontarolo,et al.  Systematic review with meta-analysis of the accuracy of diagnostic tests for COVID-19 , 2020, American Journal of Infection Control.

[37]  Long Jiang Zhang,et al.  Coronavirus Disease 2019 (COVID-19): A Perspective from China , 2020, Radiology.

[38]  A. Tahamtan,et al.  Real-time RT-PCR in COVID-19 detection: issues affecting the results , 2020, Expert review of molecular diagnostics.

[39]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[40]  Dilbag Singh,et al.  Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning , 2020, Journal of biomolecular structure & dynamics.

[41]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[42]  E. Zenteno,et al.  Should RT‐PCR be considered a gold standard in the diagnosis of COVID‐19? , 2020, Journal of medical virology.

[43]  Xiaohu Zheng,et al.  Effective treatment of severe COVID-19 patients with tocilizumab , 2020, Proceedings of the National Academy of Sciences.

[44]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[45]  Marios Anthimopoulos,et al.  Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network , 2016, IEEE Transactions on Medical Imaging.

[46]  Abien Fred Agarap.  Deep Learning using Rectified Linear Units (ReLU) , 2018, ArXiv.

[47]  Gyanendra K. Verma,et al.  Convolutional neural network: a review of models, methodologies and applications to object detection , 2019, Progress in Artificial Intelligence.

[48]  Q. Tao,et al.  Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases , 2020, Radiology.

[49]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[50]  Mohammadreza Ghorvei,et al.  Screening of COVID-19 based on the extracted radiomics features from chest CT images. , 2021, Journal of X-ray science and technology.