mlsanityData to 7 Oct 2026

CCTCOVID: COVID-19 Detection from Chest X-Ray Images Using Compact Convolutional Transformers

arXiv 2209.13399 ↗First posted 27 Sep 2022eess.IV cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

COVID-19 is a novel virus that attacks the upper respiratory tract and the lungs. Its person-to-person transmissibility is considerably rapid and this has caused serious problems in approximately every facet of individuals lives. While some infected individuals may remain completely asymptomatic, others have been frequently witnessed to have mild to severe symptoms. In addition to this, thousands of death cases around the globe indicated that detecting COVID-19 is an urgent demand in the communities. Practically, this is prominently done with the help of screening medical images such as Computed Tomography (CT) and X-ray images. However, the cumbersome clinical procedures and a large number of daily cases have imposed great challenges on medical practitioners. Deep Learning-based approaches have demonstrated a profound potential in a wide range of medical tasks. As a result, we introduce a transformer-based method for automatically detecting COVID-19 from X-ray images using Compact Convolutional Transformers (CCT). Our extensive experiments prove the efficacy of the proposed method with an accuracy of 98% which outperforms the previous works.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 72 references that are in the map. First posted on arXiv 27 Sep 2022.

Builds on

18 of 72 references.

References · 18

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,201
  5. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
  6. 2021Transformers in Vision: A Surveycited by 1,009
  7. 2018ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardwarecited by 666
  8. 2015An Introduction to Convolutional Neural Networkscited by 510
  9. 2017CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learningcited by 459
  10. 2020COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Imagescited by 264
  11. 2021Escaping the Big Data Paradigm with Compact Transformerscited by 261
  12. 2020Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networkscited by 86
6 more
  1. 2020Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Surveycited by 21
  2. 2021Vision Transformer based COVID-19 Detection using Chest X-rayscited by 14
  3. 2021Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray imagescited by 10
  4. 2021Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoostcited by 8
  5. 2021Combining a Convolutional Neural Network with Autoencoders to Predict the Survival Chance of COVID-19 Patientscited by 5
  6. 2021GSVMA: A Genetic-Support Vector Machine-Anova method for CAD diagnosis based on Z-Alizadeh Sani datasetcited by 2

Built on by

Not cited yet.