Login Register Cart 0
Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Deep Transfer Learning for COVID-19 Prediction: Case Study for Limited Data Problems

Author(s): Saleh Albahli* and Waleed Albattah

Volume 17, Issue 8, 2021

Published on: 23 November, 2020

Page: [973 - 980] Pages: 8

DOI: 10.2174/1573405616666201123120417

Abstract

Objective: Automatic prediction of COVID-19 using deep convolution neural networks based pre-trained transfer models and Chest X-ray images.

Methods: This research employs the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of the disease. Using Deep Learning models, the research aims at evaluating the effectiveness and accuracy of different convolutional neural networks models in the automatic diagnosis of COVID-19 from X-ray images as compared to diagnosis performed by experts in the medical community.

Results: Due to the fact that the dataset available for COVID-19 is still limited, the best model to use is the InceptionNetV3. Performance results show that the InceptionNetV3 model yielded the highest accuracy of 98.63% (with data augmentation) and 98.90% (without data augmentation) among the three models designed. However, as the dataset gets bigger, the Inception ResNetV2 and NASNetlarge will do a better job of classification. All the performed networks tend to over-fit when data augmentation is not used, this is due to the small amount of data used for training and validation.

Conclusion: A deep transfer learning is proposed to detecting the COVID-19 automatically from chest X-ray by training it with X-ray images gotten from both COVID-19 patients and people with normal chest X-rays. The study is aimed at helping doctors in making decisions in their clinical practice due its high performance and effectiveness, the study also gives an insight to how transfer learning was used to automatically detect the COVID-19.

Keywords: Deep transfer learning, coronavirus, X-ray, CNN, inceptioNetV3, inceptionResNetV2.

Graphical Abstract

[1]
World Health Organization (WHO) announces COVID-19 outbreak a pandemic. 2020. Available at: https://www.euro.who.int/en/health-topics/health-emergencies/coronavirus- covid-19/news/news/2020/3/who-announces-covid-19-outbreak-a- pandemic
[2]
Information for Healthcare Professionals about Coronavirus (COVID-19). 2020. Available at: https://www.cdc.gov/coronavirus/2019-ncov/hcp/index.html
[3]
Pham QV, Nguyen DC, Hwang WJ, Pathirana PN. Artificial intelligence (AI) and big data for coronavirus (COVID-19) pandemic: A survey on the state-of-the-arts. IEEE Access 2020; 99: 1-1.
[http://dx.doi.org/10.1109/ACCESS.2020.3031614]
[4]
Ai T, Yang Z, Hou H, et al. Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology 2020; 296(2): E32-40.
[http://dx.doi.org/10.1148/radiol.2020200642] [PMID: 32101510]
[5]
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 preprint 2003.
[6]
Albahli S. A deep neural network to distinguish COVID-19 from other chest diseases using X-ray images. Cur Med Imaging 2021; 17(1): 109-9.
[7]
Greenspan H, Van Ginneken B, Summers RM. Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE Trans Med Imaging 2016; 35(5): 1153-9.
[http://dx.doi.org/10.1109/TMI.2016.2553401]
[8]
Mayr A, Klambauer G, Unterthiner T, et al. Large-scale comparison of machine learning methods for drug target prediction on ChEMBL. Chem Sci (Camb) 2018; 9(24): 5441-51.
[http://dx.doi.org/10.1039/C8SC00148K] [PMID: 30155234]
[9]
Bui DT, Tsangaratos P, Nguyen VT, Van Liem N, Trinh PT. Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment. Catena 2020; 188: 104426.
[http://dx.doi.org/10.1016/j.catena.2019.104426]
[10]
Korotcov A, Tkachenko V, Russo DP, Ekins S. Comparison of deep learning with multiple machine learning methods and metrics using diverse drug discovery data sets. Mol Pharm 2017; 14(12): 4462-75.
[http://dx.doi.org/10.1021/acs.molpharmaceut.7b00578] [PMID: 29096442]
[11]
Caruana R. Multitask learning. Mach Learn 1997; 28(1): 41-75.
[http://dx.doi.org/10.1023/A:1007379606734]
[12]
Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 2013; 35(8): 1798-828.
[http://dx.doi.org/10.1109/TPAMI.2013.50] [PMID: 23787338]
[13]
Narin A, Kaya C, Pamuk Z. Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. arXiv preprint 2003.
[14]
Apostolopoulos ID, Mpesiana TA. Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phy Eng Sci Med 2020; 43(2): 635-40.
[15]
Kermany DS, Goldbaum M, Cai W, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 2018; 172(5): 1122-31. e9.
[http://dx.doi.org/10.1016/j.cell.2018.02.010] [PMID: 29474911]
[16]
Wang S, Kang B, Ma J, et al. A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). MedRxiv 2020.
[17]
Z Jianpeng, X Yutong. Viral pneumonia screening on chest X-ray images using confidence-aware anomaly detection. MedRxiv 2020. Online ahead of print.
[18]
Pathak Y, Shukla PK, Tiwari A, Stalin S, Singh S, Shukla PK. Deep transfer learning based classification model for COVID-19 disease. Ing Rech Biomed 2020.Online ahead of print.
[http://dx.doi.org/10.1016/j.irbm.2020.05.003] [PMID: 32837678]
[19]
Mikołajczyk A, Grochowski M. Data augmentation for improving deep learning in image classification problem. In 2018 international interdisciplinary PhD workshop (IIPhDW) 2018; 117 -22 .
[http://dx.doi.org/10.1109/IIPHDW.2018.8388338]
[20]
Arimura H, Katsuragawa S, Li Q, Ishida T, Doi K. Development of a computerized method for identifying the posteroanterior and lateral views of chest radiographs by use of a template matching technique. Med Phys 2002; 29(7): 1556-61.
[http://dx.doi.org/10.1118/1.1487426] [PMID: 12148738]
[21]
Lehmann TM, Güld O, Keysers D, Schubert H, Kohnen M, Wein BB. Determining the view of chest radiographs. J Digit Imaging 2003; 16(3): 280-91.
[http://dx.doi.org/10.1007/s10278-003-1655-x] [PMID: 14669063]
[22]
Kao EF, Lee C, Jaw TS, Hsu JS, Liu GC. Projection profile analysis for identifying different views of chest radiographs. Acad Radiol 2006; 13(4): 518-25.
[http://dx.doi.org/10.1016/j.acra.2006.01.009] [PMID: 16554233]
[23]
Yang JX, Zhang M, Liu ZH, Ba L, Gan JX, Xu SW. Detection of lung atelectasis/consolidation by ultrasound in multiple trauma patients with mechanical ventilation. Crit Ultrasound J 2009; 1(1): 13-6.
[http://dx.doi.org/10.1007/s13089-009-0003-x]
[24]
Pietka E, Huang HK. Orientation correction for chest images. J Digit Imaging 1992; 5(3): 185-9.
[http://dx.doi.org/10.1007/BF03167768] [PMID: 1520745]
[25]
Boone JM, Seshagiri S, Steiner RM. Recognition of chest radiograph orientation for picture archiving and communications systems display using neural networks. J Digit Imaging 1992; 5(3): 190-3.
[http://dx.doi.org/10.1007/BF03167769] [PMID: 1520746]
[26]
Kao EF, Lin WC, Hsu JS, Chou MC, Jaw TS, Liu GC. A computerized method for automated identification of erect posteroanterior and supine anteroposterior chest radiographs. Phys Med Biol 2011; 56(24): 7737-53.
[http://dx.doi.org/10.1088/0031-9155/56/24/004] [PMID: 22094308]
[27]
Luo H, Hao W, Foos DH, Cornelius CW. Automatic image hanging protocol for chest radiographs in PACS. IEEE Trans Inf Technol Biomed 2006; 10(2): 302-11.
[http://dx.doi.org/10.1109/TITB.2005.859872] [PMID: 16617619]
[28]
Dong Y, Pan Y, Zhang J, Xu W. Learning to read chest X-ray images from 16000+ examples using CNN. 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE). 51-7.
[http://dx.doi.org/10.1109/CHASE.2017.59]
[29]
Gao XW, James-Reynolds C, Currie E. Analysis of tuberculosis severity levels from ct pulmonary images based on enhanced residual deep learning architecture. Neurocomputing 2020; 392: 233-44.
[http://dx.doi.org/10.1016/j.neucom.2018.12.086]
[30]
Abbas A, Abdelsamea MM, Gaber MM. Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network arXiv preprint 2003.
[31]
Chen J, Wu L, Zhang J, et al. Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study. MedRxiv 2020.
[32]
Sharif Razavian A, Azizpour H, Sullivan J, Carlsson S. CNN features off-the-shelf: an astounding baseline for recognition InProceedings of the IEEE conference on computer vision and pattern recognition workshops. 806-13.
[33]
Ardakani AA, Kanafi AR, Acharya UR, Khadem N, Mohammadi A. Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. Comput Biol Med 2020; 121: 103795.
[http://dx.doi.org/10.1016/j.compbiomed.2020.103795] [PMID: 32568676]
[34]
Shin HC, Lee KI, Lee CE. Data Augmentation Method of Object Detection for Deep Learning in Maritime Image. 2020 IEEE International Conference on Big Data and Smart Computing (Big Comp). 463-6.
[http://dx.doi.org/10.1109/BigComp48618.2020.00-25]
[35]
Cohen JP, Morrison P, Dao L. COVID-19 image data collection arXiv:2003 2020; 11597. https://github.com/ieee8023/covid-chestxray-dataset
[36]
Kaggle. COVID-19 chest x-ray 2020. https://www.kaggle.com/bachrr/covid-chest-xray

Rights & Permissions Print Cite
Article Metrics
19
1
© 2024 Bentham Science Publishers | Privacy Policy