ArticleInternational journal of environmental research and public health2021
COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed, 144 citations in OpenAlex.
- Vision Transformers in Medical Imaging: a Comprehensive Review of Advancements and Applications Across Multiple Diseases.Journal of imaging informatics in medicine · 2025Review
- Artificial intelligence diagnosis and heatmap agent for mitral valve prolapse using 3D cine echocardiography.iScience · 2025Article
- The interpretable CT-based vision transformer model for preoperative prediction of clear cell renal cell carcinoma SSIGN score and outcome.Insights into imaging · 2025Article
- Multifractal analysis and support vector machine for the classification of coronaviruses and SARS-CoV-2 variants.Scientific reports · 2025Article
- Vision transformer and deep learning based weighted ensemble model for automated spine fracture type identification with GAN generated CT images.Scientific reports · 2025Article
- A prediction method for radiation proctitis based on SAM-Med2D model.Scientific reports · 2025Article
- Automated classification of chest X-rays: a deep learning approach with attention mechanisms.BMC medical imaging · 2025Article
- X-Ray Image-Based Real-Time COVID-19 Diagnosis Using Deep Neural Networks (CXR-DNNs).Journal of imaging · 2024Article
- Improving prognostic accuracy in lung transplantation using unique features of isolated human lung radiographs.NPJ digital medicine · 2024Article
- A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging.Journal of imaging · 2024Article
- Transformer models in biomedicine.BMC medical informatics and decision making · 2024Review
- Transformer-based land use and land cover classification with explainability using satellite imagery.Scientific reports · 2024Article
- Optimization of vision transformer-based detection of lung diseases from chest X-ray images.BMC medical informatics and decision making · 2024Article
- Vision transformer to differentiate between benign and malignant slices inScientific reports · 2024Article
- Article
- Deep learning framework for rapid and accurate respiratory COVID-19 prediction using chest X-ray images.Journal of King Saud University. Computer and information sciences · 2023Article
- A Review of the Role of Artificial Intelligence in Healthcare.Journal of personalized medicine · 2023Review
- Article
- Convolutional Networks and Transformers for Mammography Classification: An Experimental Study.Sensors (Basel, Switzerland) · 2023Article
- COVID-19 and pneumonia diagnosis from chest X-ray images using convolutional neural networks.Network modeling and analysis in health informatics and bioinformatics · 2023Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 5 countries.
Funding
Abstract
In the recent pandemic, accurate and rapid testing of patients remained a critical task in the diagnosis and control of COVID-19 disease spread in the healthcare industry. Because of the sudden increase in cases, most countries have faced scarcity and a low rate of testing. Chest X-rays have been shown in the literature to be a potential source of testing for COVID-19 patients, but manually checking X-ray reports is time-consuming and error-prone. Considering these limitations and the advancements in data science, we proposed a Vision Transformer-based deep learning pipeline for COVID-19 detection from chest X-ray-based imaging. Due to the lack of large data sets, we collected data from three open-source data sets of chest X-ray images and aggregated them to form a 30 K image data set, which is the largest publicly available collection of chest X-ray images in this domain to our knowledge. Our proposed transformer model effectively differentiates COVID-19 from normal chest X-rays with an accuracy of 98% along with an AUC score of 99% in the binary classification task. It distinguishes COVID-19, normal, and pneumonia patient's X-rays with an accuracy of 92% and AUC score of 98% in the Multi-class classification task. For evaluation on our data set, we fine-tuned some of the widely used models in literature, namely, EfficientNetB0, InceptionV3, Resnet50, MobileNetV3, Xception, and DenseNet-121, as baselines. Our proposed transformer model outperformed them in terms of all metrics. In addition, a Grad-CAM based visualization is created which makes our approach interpretable by radiologists and can be used to monitor the progression of the disease in the affected lungs, assisting healthcare.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.