FBSED based automatic diagnosis of COVID-19 using X-ray and CT images

https://doi.org/10.1016/j.compbiomed.2021.104454Get rights and content

Highlights

  • Fourier-Bessel series expansion-based image decomposition is introduced.

  • Pneumonia caused by COVID-19 and other viral pneumonia are detected using CT and X-ray images.

  • The performance of different CNNs and classifiers is studied.

  • Different combinations of channels (sub-band images) have been analyzed to make the process computationally efficient.

Abstract

This work introduces the Fourier-Bessel series expansion-based decomposition (FBSED) method, which is an implementation of the wavelet packet decomposition approach in the Fourier-Bessel series expansion domain. The proposed method has been used for the diagnosis of pneumonia caused by the 2019 novel coronavirus disease (COVID-19) using chest X-ray image (CXI) and chest computer tomography image (CCTI). The FBSED method is used to decompose CXI and CCTI into sub-band images (SBIs). The SBIs are then used to train various pre-trained convolutional neural network (CNN) models separately using a transfer learning approach. The combination of SBI and CNN is termed as one channel. Deep features from each channel are fused to get a feature vector. Different classifiers are used to classify pneumonia caused by COVID-19 from other viral and bacterial pneumonia and healthy subjects with the extracted feature vector. The different combinations of channels have also been analyzed to make the process computationally efficient. For CXI and CCTI databases, the best performance has been obtained with only one and four channels, respectively. The proposed model was evaluated using 5-fold and 10-fold cross-validation processes. The average accuracy for the CXI database was 100% for both 5-fold and 10-fold cross-validation processes, and for the CCTI database, it is 97.6% for the 5-fold cross-validation process. Therefore, the proposed method may be used by radiologists to rapidly diagnose patients with COVID-19.

Keywords

COVID-19
CT images
FBSED method
Image decomposition
X-ray image

Cited by (0)

Pradeep Kumar Chaudhary received B.E. degree in Electrical and Electronics Engineering from Rajiv Gandhi Technological University, Bhopal, India in 2016 and M.Tech degree in Electrical Engineering from National Institute of Technology Hamirpur, India in 2018. Currently he is pursuing Ph.D in Electrical Engineering from Indian Institute of Technology Indore, Indore, India. His current research interests include medical signal processing, image processing, and machine learning. He has published several research papers for reputed international journals and conference papers. He served as a reviewer in Biomedical Signal Processing and Control and IEEE Sensor Journal.

Ram Bilas Pachori received the B.E. degree with honours in Electronics and Communication Engineering from Rajiv Gandhi Technological University, Bhopal, India in 2001, the M.Tech. and Ph.D. degrees in Electrical Engineering from Indian Institute of Technology (IIT) Kanpur, Kanpur, India in 2003 and 2008, respectively. He worked as a Postdoctoral Fellow at Charles Delaunay Institute, University of Technology of Troyes, France during 2007–2008. He served as an Assistant Professor at Communication Research Center, International Institute of Information Technology, Hyderabad, India during 2008–2009. He served as an Assistant Professor at Department of Electrical Engineering, IIT Indore, Indore, India during 2009–2013. He worked as an Associate Professor at Department of Electrical Engineering, IIT Indore, Indore, India during 2013–2017 where presently he has been working as a Professor since 2017. He is also an Associated Faculty with Department of Biosciences and Biomedical Engineering and Center for Advanced Electronics at IIT Indore. He was a Visiting Professor at School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, Subang Jaya, Malaysia during 2018–2019. He worked as a Visiting Scholar at Intelligent Systems Research Center, Ulster University, Northern Ireland, UK during December 2014. He is an Associate Editor of Electronics Letters, Biomedical Signal Processing and Control journal and an Editor of IETE Technical Review journal. He is a senior member of IEEE and a Fellow of IETE and IET. He has supervised 12 Ph.D., 20 M.Tech., and 37 B.Tech. students for their theses and projects. He has 220 publications to his credit which include journal papers (132), conference papers (66), books (04), and book chapters (18). His publications have around 8000 citations with h index of 47 (Google Scholar, April 2021). He has been listed in the top h index scientists in the area of Computer Science and Electronics by Guide2Research website. He has been listed in the world's top 2% scientists in the study carried out at Stanford University, USA. He has served on review boards for more than 100 scientific journals and served for scientific committees of various national and international conferences. He has delivered more than 140 lectures in various conferences, workshops, short term courses, and institutes. His research interests are in the areas of Signal and Image Processing, Biomedical Signal Processing, Non-stationary Signal Processing, Speech Signal Processing, Brain-Computer Interfacing, Machine Learning, and Artificial Intelligence in Healthcare.

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