Study of Vision Transformers for Covid-19 Detection from Chest X-rays
Sandeep Angara* and Sharath Thirunagaru
Department of Computer Sciences, USA
*Corresponding Author: Sandeep Angara, Department of Computer Sciences, USA
July 06, 2023; Published: July 21, 2023
The COVID-19 pandemic has led to a global health crisis, highlighting the need for rapid and accurate virus detection. This research paper examines transfer learning with vision transformers for COVID-19 detection, known for its excellent performance in image recognition tasks. We leverage the capability of Vision Transformers to capture global context and learn complex patterns from chest X-ray images. In this work, we explored the recent state-of-art transformer models to detect Covid-19 using CXR images such as vision transformer (ViT), Swin-transformer, Max vision transformer (MViT), and Pyramid Vision transformer (PVT). Through the utilization of transfer learning with IMAGENET weights, the models achieved an impressive accuracy range of 98.75% to 99.5%. Our experiments demonstrate that Vision Transformers achieve state-ofthe-art performance in COVID-19 detection, outperforming traditional methods and even Convolutional Neural Networks (CNNs). The results highlight the potential of Vision Transformers as a powerful tool for COVID-19 detection, with implications for improving the efficiency and accuracy of screening and diagnosis in clinical settings.
Keywords: Vision Transformers; Swin-Transformers; Covid-19; Chest X-rays
- C Huang., et al. “Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China”. The Lancet10223 (2020).
- H Guo., et al. “The impact of the COVID-19 epidemic on the utilization of emergency dental services”. Journal of Dental Sciences 4 (2020).
- T Struyf., et al. “Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19”. Cochrane Database of Systematic Reviews2 (2021).
- BD Kevadiya., et al. “Diagnostics for SARS-CoV-2 infections”. Nature Materials5 (2021).
- G Rong., et al. “COVID-19 Diagnostic Methods and Detection Techniques”. in Encyclopedia of Sensors and Biosensors (2023).
- J Dinga., et al. “WITHDRAWN: Experience on radiological examinations and infection prevention for COVID-19 in radiology department”. Radiology of Infectious Diseases (2020).
- MY Ng., et al. “Imaging profile of the covid-19 infection: Radiologic findings and literature review”. Radiology: Cardiothoracic Imaging 1 (2020).
- LJM Kroft., et al. “Added value of ultra-low-dose computed tomography, dose equivalent to chest x-ray radiography, for diagnosing chest pathology”. Journal of Thoracic Imaging 3 (2019).
- IU Khan and N Aslam. “A deep-learning-based framework for automated diagnosis of COVID-19 using X-ray images”. Information (Switzerland)9 (2020).
- M Z Islam., et al. “A combined deep CNNLSTM network for the detection of novel coronavirus (COVID-19) using X-ray images”. Informatics in Medicine Unlocked 20 (2020).
- P K Sethy., et al. “Computer aid screening of COVID-19 using X-ray and CT scan images: An inner comparison”. Journal of X-Ray Science and Technology 2 (2021).
- P K Chaudhary and R B Pachori. “FBSED based automatic diagnosis of COVID-19 using X-ray and CT images”. Computers in Biology and Medicine 134 (2021).
- A Narin., et al. “Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks”. Pattern Analysis and Applications3 (2021).
- Prof NP Tembhare., et al. “Chest X-ray Analysis using Deep Learning”. International Journal for Research in Applied Science and Engineering Technology 1 (2023).
- F Ślazyk., et al. “CXR-FL: Deep Learning-Based Chest X-ray Image Analysis Using Federated Learning”. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2022).
- E Çallı., et al. “Deep learning for chest X-ray analysis: A survey”. Medical Image Analysis 72 (2021).
- J Feng and J Jiang. “Deep Learning-Based Chest CT Image Features in Diagnosis of Lung Cancer”. Computational and Mathematical Methods in Medicine 2022, (2022).
- C Yang., et al. “Deep learning in CT image segmentation of cervical cancer: a systematic review and meta-analysis”. Radiation Oncology1 (2022).
- X Fan., et al. “Deep Learning on MRI Images for Diagnosis of Lung Cancer Spinal Bone Metastasis”. Contrast Media and Molecular Imaging 2021 (2021).
- W Shao., et al. “ProsRegNet: A deep learning framework for registration of MRI and histopathology images of the prostate”. Medical Image Analysis 68 (2021).
- MA Naser and M J Deen. “Brain tumor segmentation and grading of lower-grade glioma using deep learning in MRI images”. Computers in Biology and Medicine 121 (2020).
- J Liu., et al. “Applications of deep learning to MRI Images: A survey”. Big Data Mining and Analytics1 (2018).
- T Imaizumi., et al. “Deep learning based 3-dimensional liver motion estimation using 2-dimensional ultrasound images”. in 2019 IEEE International Conference on Cyborg and Bionic Systems, CBS 2019, (2019).
- A Negi., et al. “RDA-UNET-WGAN: An Accurate Breast Ultrasound Lesion Segmentation Using Wasserstein Generative Adversarial Networks”. The Arabian Journal for Science and Engineering 8 (2020).
- L Wang., et al. “COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images”. Scientific Report1 (2020).
- N Das N., et al. “Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays”. Irbm (2020).
- K S Krishnan and K S Krishnan. “Vision Transformer based COVID-19 Detection using Chest X-rays”. in Proceedings of IEEE International Conference on Signal Processing, Computing and Control, (2021).
- S Guefrechi., et al. “Deep learning based detection of COVID-19 from chest X-ray images”. Multimedia Tools and Applications 21–23 (2021).
- K Simonyan and A Zisserman. “Very deep convolutional networks for large-scale image recognition”. in 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, (2015).
- K He., et al. “Deep residual learning for image recognition”. in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (2016).
- C Szegedy., et al. “Rethinking the Inception Architecture for Computer Vision”. in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2016).
- M Pavlova., et al. “COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest X-ray Images”. Frontiers in Medicine (Lausanne) 9 (2022).
- G Li., et al. “COVID-19 Detection Based on Self-Supervised Transfer Learning Using Chest X-Ray Images”. Dec. (2022).
- J Park., et al. “A Deep Learning Model with Self Supervised Learning and Attention Mechanism for COVID-19 Diagnosis Using Chest X-ray Images”. Electronics (Basel)16 (1996): 2021.
- D Shome., et al. “Covid-transformer: Interpretable covid-19 detection using vision transformer for healthcare”. International Journal of Environmental Research and Public Health 21 (2021).
- SM Anwar., et al. “SS-CXR: Multitask Representation Learning using Self Supervised Pre-training from Chest X-Rays”. Nov. (2022).
- A Dosovitskiy., et al. “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. Oct. (2020).
- Z Liu., et al. “Swin Transformer: Hierarchical Vision Transformer using Shifted Windows”. Mar. (2021).
- H Fan., et al. “Multiscale Vision Transformers”. Apr. (2021).
- W Wang., et al. “PVT v2: Improved Baselines with Pyramid Vision Transformer”. Jun. (2021).
- M Pavlova., et al. “COVIDx CXR-3: A Large-Scale, Open-Source Benchmark Dataset of Chest X-ray Images for Computer-Aided COVID-19 Diagnostics”. Jun. (2022).
- A Vaswani., et al. “Attention Is All You Need”. Jun. (2017).
- F Shamshad., et al. “Transformers in Medical Imaging: A Survey”. Jan. (2022).
- S Angara., et al. “An Empirical Study of Vision Transformers for Cervical Precancer Detection”. in Communications in Computer and Information Science, (2022).
- ZR Murphy., et al. “Visual Transformers and Convolutional Neural Networks for Disease Classification on Radiographs: A Comparison of Performance, Sample Efficiency, and Hidden Stratification”. Radiology: Artificial Intelligence6 (2022).
- Z Tu., et al. “MaxViT: Multi-Axis Vision Transformer”. Apr. (2022).
- W Wang., et al. “Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions”. Feb. (2021).
- , et al. “ImageNet Large Scale Visual Recognition Challenge”. Sep. (2014).
- DP Kingma and J Ba. “Adam: A Method for Stochastic Optimization”. (2014).
- I Loshchilov and F Hutter. “SGDR: Stochastic Gradient Descent with Warm Restarts”. (2016).
- R Wightman., et al. “rwightman/pytorch-image-models: v0.8.10dev0 Release”. (2023).
- H Aboutalebi., et al. “MEDUSA: Multi-scale Encoder-Decoder SelfAttention Deep Neural Network Architecture for Medical Image Analysis”. (2021).