The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks
Open accessAbstract
Coronavirus is an epidemic that spreads very quickly. For this reason, it has very devastating effects in many areas worldwide. It is vital to detect COVID-19 diseases as quickly as possible to restrain the spread of the disease. The similarity of COVID-19 disease with other lung infections makes the diagnosis difficult. In addition, the high spreading rate of COVID-19 increased the need for a fast system for the diagnosis of cases. For this purpose, interest in various computer-aided (such as CNN, DNN, etc.) deep learning models has been increased. In these models, mostly radiology images are applied to determine the positive cases. Recent studies show that, radiological images contain important information in the detection of coronavirus. In this study, a novel artificial neural network, Convolutional CapsNet for the detection of COVID-19 disease is proposed by using chest X-ray images with capsule networks. The proposed approach is designed to provide fast and accurate diagnostics for COVID-19 diseases with binary classification (COVID-19, and No-Findings), and multi-class classification (COVID-19, and No-Findings, and Pneumonia). The proposed method achieved an accuracy of 97.24%, and 84.22% for binary class, and multi-class, respectively. It is thought that the proposed method may help physicians to diagnose COVID-19 disease and increase the diagnostic performance. In addition, we believe that the proposed method may be an alternative method to diagnose COVID-19 by providing fast screening.
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“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 [279]
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Clinical characteristics of coronavirus disease 2019 in China
Guan, W.-J., Ni, Z.-Y., Hu, Y., Liang, W.-H., Ou, C.-Q., He, J.-X., Liu, L., Shan, H., Lei, C.-L., Hui, D. S. C., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2002032 · Cited by 30,246
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
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.369 · Cited by 4,616
Dynamic Routing Between Capsules
Sabour, S., Frosst, N., & Hinton, G. E. (2017) · arXiv · doi:10.48550/arxiv.1710.09829 · Cited by 4,167
Wang, L., Lin, Z. Q., & Wong, A. M. (2020) · Scientific Reports · doi:10.1038/s41598-020-76550-z · Cited by 3,228
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 2,652
Apostolopoulos, I. D., & Mpesiana, T. A. (2020) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-020-00865-4 · Cited by 2,338
Coronavirus Disease 2019 (COVID-19): A Perspective from China
Zu, Z. Y., Jiang, M. D., Xu, P. P., Chen, W., Ni, Q. Q., Lu, G. M., & Zhang, L. J. (2020) · Radiology · doi:10.1148/radiol.2020200490 · Cited by 2,070
Narin, A., Kaya, C., & Pamuk, Z. (2021) · Pattern Analysis and Applications · doi:10.1007/s10044-021-00984-y · Cited by 2,054
Critical Care Utilization for the COVID-19 Outbreak in Lombardy, Italy
Grasselli, G., Pesenti, A., & Cecconi, M. (2020) · JAMA · doi:10.1001/jama.2020.4031 · Cited by 2,019
Research works printed but not linked: 1
- Rajput, K.S., Wibowo, S., Hao, C., and Majmudar, M.On arrhythmia detection by deep learning and multidimensional representation, arXiv, 190400138, 2019.
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Jhansi, B., & Ramesh, M. (2023) · AIP conference proceedings · doi:10.1063/5.0179105
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Abubakr, M., Amjad, U., Raza, A., Khurram, M., Farman, H., & Ali, A. (2023) · Pakistan Journal of Engineering Technology & Science · doi:10.22555/pjets.v11i1.999
Cheraghi, S., Amiri, S., Abdolali, F., Esfahani, A. J., Tehranizadeh, A. A., Ahadi, R., Ansari, F., Nafchi, E. R., & Hormozi‐Moghaddam, Z. (2024) · International Journal of Radiation Research · doi:10.61186/ijrr.22.1.55
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Alrusaini, O. A. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.0150493
Santhosh Kumar, S. (2024) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem35489
Nasip, Ö. F. (2024) · Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi · doi:10.17714/gumusfenbil.1487192
Digit Recognition and Separation of Overlapped Digits using Deep CapsNet
Moudgil, A., Sareen, B., Ghantasala, G. S. P., Rajesh Sharma, R., Sungheetha, A., Soubache, I. D., & Vidyullatha, P. (2024) · International Conference on Computational and Characterization Techniques in Engineering & Sciences (IC3TES) · doi:10.1109/ic3tes62412.2024.10877473
Exploring Recent Developments in Radiographic Chest Disease Detection Through Deep Learning Models
Godbin, A. B., Jasmine, S. G., & Narendranathan, S. K. (2025) · Algorithms for intelligent systems · doi:10.1007/978-981-96-1285-7_9
Using sanity checks in deep neural networks predictions
Alboaridhee, Z. J., Salih, H. S., Al-Augby, S., Ali, M. H., & Ghandour, A. M. (2025) · AIP conference proceedings · doi:10.1063/5.0274103
Kumar, A., & Gupta, S. (2026) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-026-03446-y
Wu, X., Chunyu, N., Feng, L., & Zheng, J. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105705 · Cited by 1
IGF ‐ CNN : An Optimized Deep Learning Model for Covid‐19 Classification
Tiwari, V., Singh, S. K., Hassan, U., & Singhal, A. (2025) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.70247
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Ushasri, C., & Ketepalli, G. (2025) · International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS) · doi:10.1109/icuis67429.2025.11380471
Theng, D. P., Singh, S., & Hiwale, M. (2025) · IEEE Pune Section International Conference (PuneCon) · doi:10.1109/punecon67554.2025.11377775
Dash, D., Karaddi, S. H., Sharma, D., & Sharma, L. D. (2026) · Scientific Reports · doi:10.1038/s41598-026-47815-w
Ouchicha, C. (2026) · WSEAS Transactions on Circuits and Systems archive · doi:10.37394/23201.2026.25.7
Chen, M., Gu, Y., Wang, M., Yang, L., Zhang, B., Li, J., Liu, X., Hao, J., Ma, H., Zhang, W., et al. (2026) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.70367
Srivastava, S., & Singh, Y. N. (2026) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.70742
Chitradevi, T. N., Swathypriyadharsini, P., Dhanusri, M., Nandhitha, K., Hemalatha, G., & Dhavamani, S. (2026) · International conference Computer Networks and Inventive Communication Technologies · doi:10.1109/iccnct68477.2026.11590114
Pramesti, R. D., Handajani, S. S., Sulandari, W., Pratiwi, H., Pardede, H. F., & Zilvan, V. (2026) · Springer eBooks · doi:10.1007/978-3-032-32458-0_11
Optimized approach to image-based automatic disease classification using improved capsule networks
Yadav, S., & Dhage, S. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21868-5
Metadata
- Authors
- Toraman, S., Alakus, T. B., & Turkoglu, I.
- Affiliations
- Fırat University (TR)
- Venue
- Chaos, Solitons & Fractals
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 140
- Pages
- 110122
- ISSN
- 0960-0779 (ISSN-L)
- PMC
- PMC7357532
- OpenAlex
- W3041148517
- Cited by
- 327
- References
- 60
- Citations by year
- 2026: 8 · 2025: 20 · 2024: 29 · 2023: 67 · 2022: 81 · 2021: 106 · 2020: 15
- FWCI
- 32.58
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Chaos Solitons & Fractals (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Kırklareli University Institutional Repository (Kırklareli University) (submittedVersion, other-oa)
- DSpace - FIRAT (Fırat University) (submittedVersion, cc-by-nc-nd)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- COVID-19 detection, chest X-ray images, capsule networks, computer-aided diagnosis, pneumonia classification, binary classification, multi-class classification, lung infection diagnosis
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