COVID-19 Detection in Chest X-ray Images Using Swin-Transformer and Transformer in Transformer
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Abstract (arXiv)
The Coronavirus Disease 2019 (COVID-19) has spread globally and caused serious damage. Chest X-ray images are widely used for COVID-19 diagnosis and the Artificial Intelligence method can increase efficiency and accuracy. In the Challenge of Chest XR COVID-19 detection in Ethics and Explainability for Responsible Data Science (EE-RDS) conference 2021, we proposed a method that combined Swin Transformer and Transformer in Transformer to classify chest X-ray images as three classes: COVID-19, Pneumonia, and Normal (healthy) and achieved 0.9475 accuracies on the test set.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 7 of its 20 references that are in the map. First posted on arXiv 16 Oct 2021.
Builds on
7 of 20 references.
References · 7
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
- 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
- 2020COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Imagescited by 264
- 2020Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learningcited by 67
- 2020Deep Learning for Screening COVID-19 using Chest X-Ray Imagescited by 16
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Citing papers per year: 2022: 3; 2023: 0; 2024: 0; 2025: 2; 2026: 1.
6 papers.
- 2022Transformers in Medical Imaging: A Surveycited by 245
- 2022A survey on attention mechanisms for medical applications: are we moving towards better algorithms?cited by 15
- 2022Spatiotemporal Feature Learning Based on Two-Step LSTM and Transformer for CT Scanscited by 7
- 2025CAP-LLM: Context-Augmented Personalized Large Language Models for News Headline Generationcited by 1
- 2025VisuCraft: Enhancing Large Vision-Language Models for Complex Visual-Guided Creative Content Generation via Structured Information Extractioncited by 0
- 2026A Hybrid Federated Learning Based Ensemble Approach for Lung Disease Diagnosis Leveraging Fusion of SWIN Transformer and CNNcited by 0
- 2026A Hybrid Federated Learning Based Ensemble Approach for Lung Disease Diagnosis Leveraging Fusion of SWIN Transformer and CNN19 Feb 2026
- 2025CAP-LLM: Context-Augmented Personalized Large Language Models for News Headline Generation5 Aug 2025
- 2025VisuCraft: Enhancing Large Vision-Language Models for Complex Visual-Guided Creative Content Generation via Structured Information Extraction4 Aug 2025
- 2022Spatiotemporal Feature Learning Based on Two-Step LSTM and Transformer for CT Scans4 Jul 2022
- 2022A survey on attention mechanisms for medical applications: are we moving towards better algorithms?26 Apr 2022
- 2022Transformers in Medical Imaging: A Survey24 Jan 2022