CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization
Open accessAbstract
With the recent outbreak of COVID-19, fast diagnostic testing has become one of the major challenges due to the critical shortage of test kit. Pneumonia, a major effect of COVID-19, needs to be urgently diagnosed along with its underlying reasons. In this paper, deep learning aided automated COVID-19 and other pneumonia detection schemes are proposed utilizing a small amount of COVID-19 chest X-rays. A deep convolutional neural network (CNN) based architecture, named as CovXNet, is proposed that utilizes depthwise convolution with varying dilation rates for efficiently extracting diversified features from chest X-rays. Since the chest X-ray images corresponding to COVID-19 caused pneumonia and other traditional pneumonias have significant similarities, at first, a large number of chest X-rays corresponding to normal and (viral/bacterial) pneumonia patients are used to train the proposed CovXNet. Learning of this initial training phase is transferred with some additional fine-tuning layers that are further trained with a smaller number of chest X-rays corresponding to COVID-19 and other pneumonia patients. In the proposed method, different forms of CovXNets are designed and trained with X-ray images of various resolutions and for further optimization of their predictions, a stacking algorithm is employed. Finally, a gradient-based discriminative localization is integrated to distinguish the abnormal regions of X-ray images referring to different types of pneumonia. Extensive experimentations using two different datasets provide very satisfactory detection performance with accuracy of 97.4% for COVID/Normal, 96.9% for COVID/Viral pneumonia, 94.7% for COVID/Bacterial pneumonia, and 90.2% for multiclass COVID/normal/Viral/Bacterial pneumonias. Hence, the proposed schemes can serve as an efficient tool in the current state of COVID-19 pandemic. All the architectures are made publicly available at: https://github.com/Perceptron21/CovXNet.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Several convolutional neural network approaches for automatic detection from X-ray images in COVID-19 and pulmonary disease showed excellent performance.
“In the area of COVID-19 and pulmonary disease, several convolutional neural network approaches have been proposed for automatic detection from X-ray images which showed an excellent performance [46, 276–279].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [277]
CovXNet multiclass classification (COVID, normal, viral or bacterial pneumonia) achieved an accuracy of 90.2%.
“The multiclass classification (COVID, normal, viral or bacterial pneumonia) achieved an accuracy of 90.2%.”
From Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks (2023) · cites this paper as [160]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138 · see citing works
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513 · see citing works
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380 · see citing works
Multi-scale context aggregation by dilated convolutions
Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738 · see citing works
Sohrabi, C., Alsafi, Z., O’Neill, N., Khan, M., Kerwan, A., Al‐Jabir, A., Iosifidis, C., & Agha, R. (2020) · International Journal of Surgery · doi:10.1016/j.ijsu.2020.02.034 · Cited by 6,093 · see citing works
Lai, C.-C., Shih, T.-P., Ko, W.-C., Tang, H.-J., & Hsueh, P.-R. (2020) · International Journal of Antimicrobial Agents · doi:10.1016/j.ijantimicag.2020.105924 · Cited by 6,068 · see citing works
The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak
Rothan, H. A., & Byrareddy, S. N. (2020) · Journal of Autoimmunity · doi:10.1016/j.jaut.2020.102433 · Cited by 5,840 · see citing works
Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.010 · Cited by 4,842 · see citing works
Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study
Shi, H., Han, X., Jiang, N., Cao, Y., Alwalid, O., Gu, J., Fan, Y., & Zheng, C. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30086-4 · Cited by 3,664 · see citing works
Wang, L., Lin, Z. Q., & Wong, A. M. (2020) · Scientific Reports · doi:10.1038/s41598-020-76550-z · Cited by 3,151 · see citing works
CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV)
Chung, M., Bernheim, A., Mei, X., Zhang, N., Huang, M., Zeng, X., Cui, J., Xu, W., Yang, Y., Fayad, Z. A., et al. (2020) · Radiology · doi:10.1148/radiol.2020200230 · Cited by 2,751 · see citing works
Automated detection of COVID-19 cases using deep neural networks with X-ray images
Öztürk, T., Talo, M., Yildirim, E. A., Baloğlu, U. B., Yıldırım, Ö., & Acharya, U. R. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103792 · Cited by 2,607 · see citing works
Apostolopoulos, I. D., & Mpesiana, T. A. (2020) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-020-00865-4 · Cited by 2,338 · see citing works
Narin, A., Kaya, C., & Pamuk, Z. (2021) · Pattern Analysis and Applications · doi:10.1007/s10044-021-00984-y · Cited by 2,054 · see citing works
Xie, X., Zhong, Z., Zhao, W., Zheng, C., Wang, F., & Liu, J. (2020) · Radiology · doi:10.1148/radiol.2020200343 · Cited by 1,960 · see citing works
Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., Bai, J., Lu, Y., Fang, Z., Song, Q., et al. (2020) · Radiology · doi:10.1148/radiol.2020200905 · Cited by 1,927 · see citing works
Bai, H. X., Hsieh, B., Xiong, Z., Halsey, K., Choi, J. W., Tran, T. M. L., Pan, I., Shi, L.-B., Wang, D., Ji, M., et al. (2020) · Radiology · doi:10.1148/radiol.2020200823 · Cited by 1,232 · see citing works
Imaging Profile of the COVID-19 Infection: Radiologic Findings and Literature Review
Ng, M., Lee, E. Y. P., Jin, Y., Yang, F., Li, X., Wang, H., Lui, M. M., Lo, C. S.-Y., Leung, B., Khong, P., et al. (2020) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.2020200034 · Cited by 1,141 · see citing works
Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification
Kermany, D. (2018) · Data Archiving and Networked Services (DANS) · doi:10.17632/rscbjbr9sj.2 · Cited by 758 · see citing works
A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images
Chouhan, V., Singh, S. K., Khamparia, A., Gupta, D., Tiwari, P., Moreira, C., Damaševičius, R., & de Albuquerque, V. H. C. (2020) · Applied Sciences · doi:10.3390/app10020559 · Cited by 753 · see citing works
Detection of Coronavirus Disease (COVID-19) Based on Deep FeaturesPreprint
Sethy, P. K., & Behera, S. K. (2020) · Preprints.org · doi:10.20944/preprints202003.0300.v1 · Cited by 723 · see citing works
Radiographic and CT Features of Viral Pneumonia
Koo, H. J., Lim, S., Choe, J., Choi, S., Sung, H., & Do, K. (2018) · Radiographics · doi:10.1148/rg.2018170048 · Cited by 712 · see citing works
Gozes, O., Frid-Adar, M., Greenspan, H. K., Browning, P. D., Zhang, H., Ji, W., Bernheim, A., & Siegel, E. L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.05037 · Cited by 638 · see citing works
Imaging of Pulmonary Viral Pneumonia
Franquet, T. (2011) · Radiology · doi:10.1148/radiol.11092149 · Cited by 380 · see citing works
Rajaraman, S., Candemir, S., Kim, I., Thoma, G. R., & Antani, S. (2018) · Applied Sciences · doi:10.3390/app8101715 · Cited by 319 · see citing works
Imaging of pneumonia: trends and algorithms
Franquet, T. (2001) · European Respiratory Journal · doi:10.1183/09031936.01.00213501 · Cited by 304 · see citing works
Radiology of bacterial pneumonia
Vilar, J., Domingo, M. L., Soto, C., et al. (2004) · European Journal of Radiology · doi:10.1016/j.ejrad.2004.03.010 · Cited by 115 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906 · see citing works
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
van der Velden, B. H. M., Kuijf, H. J., Gilhuijs, K. G. A., & Viergever, M. A. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102470 · Cited by 1,328 · see citing works
Nazir, S., Dickson, D. M., & Akram, M. U. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106668 · Cited by 283 · see citing works
“A CNN model CovXNet was used for binary and multiclass classification with depth-wise convolution with varying dilation rates that aided extraction of diverse features from CXR [160].”
MethodApplications of artificial intelligence in battling against covid-19: A literature review
Tayarani N., M.-H. (2021) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110338 · Cited by 210 · see citing works
COVID-19 image classification using deep learning: Advances, challenges and opportunities
Aggarwal, P., Mishra, N. K., Fatimah, B., Singh, P., Gupta, A., & Joshi, S. D. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105350 · Cited by 168 · see citing works
- Authors
- Mahmud, T., Rahman, M. A., & Fattah, S. A.
- Affiliations
- Bangladesh University of Engineering and Technology (BD)
- Corresponding author
- Fattah, Shaikh Anowarul
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 122
- Pages
- 103869
- ISSN
- 0010-4825 (ISSN-L)
- PMC
- PMC7305745
- OpenAlex
- W3036638392
- Cited by
- 525
- References
- 30
- Citations by year
- 2026: 16 · 2025: 31 · 2024: 63 · 2023: 96 · 2022: 141 · 2021: 151 · 2020: 27
- FWCI
- 50.50
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Computers in Biology and Medicine (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- COVID-19 detection, pneumonia detection, chest X-ray images, depthwise convolution, transfer learning, fine-tuning, viral pneumonia, bacterial pneumonia, automated diagnosis
- MeSH terms
- Betacoronavirus, Deep Learning, COVID-19, SARS-CoV-2, COVID-19 Testing, Algorithms, Humans, Image Processing, Computer-Assisted, Pneumonia, Viral, Radiography, Thoracic, Reproducibility of Results, Databases, Factual, Neural Networks, Computer, Coronavirus Infections, Clinical Laboratory Techniques, Pandemics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
