ArticleJournal of personalized medicine2022
COVID-19 Detection in CT/X-ray Imagery Using Vision Transformers.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review.International journal of environmental research and public health · 2022Pooled it
- Deep Learning Network Selection and Optimized Information Fusion for Enhanced COVID-19 Detection: A Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
- Optimization of vision transformer-based detection of lung diseases from chest X-ray images.BMC medical informatics and decision making · 2024Article
- Playing Flappy Bird Based on Motion Recognition Using a Transformer Model and LIDAR Sensor.Sensors (Basel, Switzerland) · 2024Article
- Detection of Severe Lung Infection on Chest Radiographs of COVID-19 Patients: Robustness of AI Models across Multi-Institutional Data.Diagnostics (Basel, Switzerland) · 2024Article
- A COVID-19 medical image classification algorithm based on Transformer.Scientific reports · 2023Article
- FibroVit-Vision transformer-based framework for detection and classification of pulmonary fibrosis from chest CT images.Frontiers in medicine · 2023Article
- CCTCOVID: COVID-19 detection from chest X-ray images using Compact Convolutional Transformers.Frontiers in public health · 2023Article
- Contrasting EfficientNet, ViT, and gMLP for COVID-19 Detection in Ultrasound Imagery.Journal of personalized medicine · 2022Article
- SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19.Biocybernetics and biomedical engineeringArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
The steady spread of the 2019 Coronavirus disease has brought about human and economic losses, imposing a new lifestyle across the world. On this point, medical imaging tests such as computed tomography (CT) and X-ray have demonstrated a sound screening potential. Deep learning methodologies have evidenced superior image analysis capabilities with respect to prior handcrafted counterparts. In this paper, we propose a novel deep learning framework for Coronavirus detection using CT and X-ray images. In particular, a Vision Transformer architecture is adopted as a backbone in the proposed network, in which a Siamese encoder is utilized. The latter is composed of two branches: one for processing the original image and another for processing an augmented view of the original image. The input images are divided into patches and fed through the encoder. The proposed framework is evaluated on public CT and X-ray datasets. The proposed system confirms its superiority over state-of-the-art methods on CT and X-ray data in terms of accuracy, precision, recall, specificity, and F1 score. Furthermore, the proposed system also exhibits good robustness when a small portion of training data is allocated.
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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 OpenQuestion graph.