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

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

9 Oct 2021arXiv:2110.04458archive 2025-07-28

Koushik Sivarama Krishnan, Karthik Sivarama Krishnan

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.

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Tasks

COVID-19 DiagnosisCOVID-19 Modelling

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
COVID-19 Diagnosis COVID-19 CXR Dataset ViT-B/32 Average F1 0.946 #1 of 1 Archive leaderboard report
COVID-19 Diagnosis COVID-19 CXR Dataset ViT-B/32 Average Precision 0.953 #1 of 1 Archive leaderboard report
COVID-19 Diagnosis COVID-19 CXR Dataset ViT-B/32 Average Recall 0.938 #1 of 1 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

AttentionDense ConnectionsLayer NormalizationLinear LayerMulti-Head AttentionResidual ConnectionSoftmaxVision Transformer

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