Covid-19 automatic diagnosis with ct images using the novel transformer architecture
Open accessAbstract
Even though vaccines are already in use worldwide, the COVID-19 pandemic is far from over, with some countries re-establishing the lockdown state, the virus has taken over 2 million lives until today, being a serious health issue. Although real-time reverse transcription-polymerase chain reaction (RTPCR) is the first tool for COVID-19 diagnosis, its high false-negative rate and low sensitivity might delay accurate diagnosis. Therefore, fast COVID-19 diagnosis and quarantine, combined with effective vaccination plans, is crucial for the pandemic to be over as soon as possible. To that end, we propose an intelligent system to classify computed tomography (CT) of lung images between a normal, pneumonia caused by something other than the coronavirus or pneumonia caused by the coronavirus. This paper aims to evaluate a complete selfattention mechanism with a Transformer network to capture COVID-19 pattern over CT images. This approach has reached the state-of-the-art in multiple NLP problems and just recently is being applied for computer vision tasks. We combine vision transformer and performer (linear attention transformers), and also a modified vision transformer, reaching 96.00% accuracy.
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?
Most works in medical image classification jointly adopt CNN and transformer but did not develop a transformer-based backbone without convolutions to capture multi-granularity information in medical images.
“Most works [30 -34] in medical image classi fi cation jointly adopt the CNN and transformer model but did not develop a transformer-based backbone without convolutions to capture multi-granularity information in medical images.”
From DilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound (2023) · cites this paper as [34]
The Introduction describes this paper’s method as a complete self-attention network, while the Conclusion says it compares transformers with classical CNNs—not that it jointly adopts them. The Proposed Method describes ViT and DeIT variants, and the paper does not present a multi-granularity backbone. (LLM, conf=0.86)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measured
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
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648 · see citing works
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 21,793 · see citing works
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Tan, M., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1905.11946 · Cited by 17,072 · see citing works
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196 · see citing works
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network
Abbas, A., Abdelsamea, M. M., & Gaber, M. M. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01829-7 · Cited by 1,142 · see citing works
COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images
Afshar, P., Heidarian, S., Naderkhani, F., Oikonomou, A., Plataniotis, K. N., & Mohammadi, A. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.09.010 · Cited by 710 · see citing works
Rethinking Attention with Performers
Choromanski, K., Likhosherstov, V., Dohan, D., Song, X., Gane, A., Sarlos, T., Hawkins, P., Davis, J., Mohiuddin, A., Kaiser, L., et al. (2020) · arXiv · doi:10.48550/arxiv.2009.14794 · Cited by 695 · cited 2× · see citing works
“We can also observe that the difference between a ViT with a classical transformer encoder and one with an efficient performer encoder is minimal, meaning that the Per-former encoder does approximate well the Transformer encoder, as stated on [4], while requiring less computing resources.”
BackgroundMethodClassification of the COVID-19 infected patients using DenseNet201 based deep transfer learning
Jaiswal, A., Gianchandani, N., Singh, D., Kumar, V., & Kaur, M. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1788642 · Cited by 612 · see citing works
Gunraj, H., Wang, L., & Wong, A. M. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2020.608525 · Cited by 336 · see citing works
Silva, P., Luz, E. J. D. S., Silva, G., Moreira, G. J. P., Silva, R., Lucio, D. R., & Menotti, D. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100427 · Cited by 254 · see citing works
Effectiveness of COVID-19 diagnosis and management tools: A review
Alsharif, W. M., & Qurashi, A. A. (2020) · Radiography · doi:10.1016/j.radi.2020.09.010 · Cited by 228 · see citing works
Training data-efficient image transformers & distillation through attention
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., & Jégou, H. (2020) · arXiv · doi:10.48550/arxiv.2012.12877 · Cited by 182 · see citing works
Hasan, A. M., Al-Jawad, M. M. H., Jalab, H. A., Shaiba, H. A., Ibrahim, R. W., & Al-shamasneh, A. R. M. (2020) · Entropy · doi:10.3390/e22050517 · Cited by 153 · see citing works
Gunraj, H., Wang, L., & Wong, A. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.05383
Flagged first, then by citations
Cited by
Sorted by citations
A Review of the Role of Artificial Intelligence in Healthcare
Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A. V., Al Muhanna, D., & Al-Muhanna, F. A. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13060951 · Cited by 915 · see citing works
DilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound
Wu, Y., Yang, Y., Zhu, L., Han, Z., Luo, H., Xue, X., & Wang, W. (2023) · Frontiers in Physics · doi:10.3389/fphy.2023.1239400 · Cited by 2 · see citing works
- Authors
- Costa, G. S. S., Paiva, A. C., Braz Júnior, G., & Ferreira, M. M.
- Affiliations
- Universidade Federal do Maranhão (BR)
- ORCID
- Paiva, Anselmo C.
- Venue
- Anais do XXI Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS 2021)
- Published
- 2021-06-15
- Pages
- 293-301
- OpenAlex
- W3200638744
- Cited by
- 24
- References
- 16
- Citations by year
- 2026: 1 · 2025: 9 · 2024: 3 · 2023: 8 · 2022: 3
- FWCI
- 4.40
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Funders
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
- Conselho Nacional de Desenvolvimento Científico e Tecnológico
- Fundação de Amparo à Pesquisa e ao Desenvolvimento Científico e Tecnológico do Maranhão
- 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 › Infectious Diseases › SARS-CoV-2 detection and testing
- Keywords
- COVID-19 diagnosis, computed tomography, lung CT images, vision transformer, self-attention, pneumonia classification, coronavirus pneumonia, automated medical image classification
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
