mlsanityData to 14 Oct 2026

Vision Transformer based COVID-19 Detection using Chest X-rays

arXiv 2110.04458 ↗First posted 9 Oct 2021eess.IV cs.CV cs.LGcited by 14

Not on the map. Why?

Abstract (arXiv)

COVID-19 is a global pandemic, and detecting them is a momentous task for medical professionals today due to its rapid mutations. Current methods of examining chest X-rays and CT scan requires profound knowledge and are time consuming, which suggests that it shrinks the precious time of medical practitioners when people's lives are at stake. This study tries to assist this process by achieving state-of-the-art performance in classifying chest X-rays by fine-tuning Vision Transformer(ViT). The proposed approach uses pretrained models, fine-tuned for detecting the presence of COVID-19 disease on chest X-rays. This approach achieves an accuracy score of 97.61%, precision score of 95.34%, recall score of 93.84% and, f1-score of 94.58%. This result signifies the performance of transformer-based models on chest X-ray.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 11 of its 22 references that are in the map. First posted on arXiv 9 Oct 2021.

Builds on

11 of 22 references.

References · 11

  1. 2017Attention Is All You Needcited by 60,361
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2016Densely Connected Convolutional Networkscited by 6,720
  5. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  6. 2016Wide Residual Networkscited by 3,070
  7. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
  8. 2021MLP-Mixer: An all-MLP Architecture for Visioncited by 1,446
  9. 2019On the Variance of the Adaptive Learning Rate and Beyondcited by 772
  10. 2018Albumentations: fast and flexible image augmentationscited by 560
  11. 2020Can AI help in screening Viral and COVID-19 pneumonia?cited by 200

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 6; 2023: 6.

14 papers.

Sort:

  1. 2022Transformers in Medical Image Analysis: A Reviewcited by 41
  2. 2022Eye-gaze-guided Vision Transformer for Rectifying Shortcut Learningcited by 20
  3. 2022Recent Progress in Transformer-based Medical Image Analysiscited by 9
  4. 2021SwiftSRGAN -- Rethinking Super-Resolution for Efficient and Real-time Inferencecited by 4
  5. 2022Benchmarking Conventional Vision Models on Neuromorphic Fall Detection and Action Recognition Datasetcited by 3
  6. 2023MFAAN: Unveiling Audio Deepfakes with a Multi-Feature Authenticity Networkcited by 2
  7. 2023Connectional-Style-Guided Contextual Representation Learning for Brain Disease Diagnosiscited by 1
  8. 2023Adaptive Input-image Normalization for Solving the Mode Collapse Problem in GAN-based X-ray Imagescited by 1
  9. 2021Weakly-supervised Generative Adversarial Networks for medical image classificationcited by 0
  10. 2022RadTex: Learning Efficient Radiograph Representations from Text Reportscited by 0
  11. 2022CCTCOVID: COVID-19 Detection from Chest X-Ray Images Using Compact Convolutional Transformerscited by 0
  12. 2023CECT: Controllable Ensemble CNN and Transformer for COVID-19 Image Classificationcited by 0
2 more
  1. 2023Advancing Ischemic Stroke Diagnosis: A Novel Two-Stage Approach for Blood Clot Origin Identificationcited by 0
  2. 2023Study of Vision Transformers for Covid-19 Detection from Chest X-rayscited by 0