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Papers › CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-19

CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-19

17 Feb 2023arXiv:2302.09004archive 2025-07-28

Tareque Rahman Ornob, Gourab Roy, Enamul Hassan

Patients with the COVID-19 infection may have pneumonia-like symptoms as well as respiratory problems which may harm the lungs. From medical images, coronavirus illness may be accurately identified and predicted using a variety of machine learning methods. Most of the published machine learning methods may need extensive hyperparameter adjustment and are unsuitable for small datasets. By leveraging the data in a comparatively small dataset, few-shot learning algorithms aim to reduce the requirement of large datasets. This inspired us to develop a few-shot learning model for early detection of COVID-19 to reduce the post-effect of this dangerous disease. The proposed architecture combines few-shot learning with an ensemble of pre-trained convolutional neural networks to extract feature vectors from CT scan images for similarity learning. The proposed Triplet Siamese Network as the few-shot learning model classified CT scan images into Normal, COVID-19, and Community-Acquired Pneumonia. The suggested model achieved an overall accuracy of 98.719%, a specificity of 99.36%, a sensitivity of 98.72%, and a ROC score of 99.9% with only 200 CT scans per category for training data.

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Tasks

COVID-19 DiagnosisComputed Tomography (CT)Few-Shot Image ClassificationFew-Shot Learning

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Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
COVID-19 Diagnosis Large COVID-19 CT scan slice dataset CovidExpert AUC-ROC 0.9992 #1 of 1 Archive leaderboard report
COVID-19 Diagnosis Large COVID-19 CT scan slice dataset CovidExpert Accuracy 0.98719 #1 of 1 Archive leaderboard report
COVID-19 Diagnosis Large COVID-19 CT scan slice dataset CovidExpert Macro F1 0.9872 #1 of 1 Archive leaderboard report
COVID-19 Diagnosis Large COVID-19 CT scan slice dataset CovidExpert Macro Precision 0.9873 #1 of 1 Archive leaderboard report
COVID-19 Diagnosis Large COVID-19 CT scan slice dataset CovidExpert Macro Recall 0.9872 #1 of 1 Archive leaderboard report
COVID-19 Diagnosis Large COVID-19 CT scan slice dataset CovidExpert Micro Precision 0.9872 #1 of 1 Archive leaderboard report
COVID-19 Diagnosis Large COVID-19 CT scan slice dataset CovidExpert Specificity 0.9936 #1 of 1 Archive leaderboard report
Few-Shot Learning Large COVID-19 CT scan slice dataset CovidExpert AUC-ROC 0.9992 #1 of 1 Archive leaderboard report
Few-Shot Learning Large COVID-19 CT scan slice dataset CovidExpert Accuracy 0.98719 #1 of 1 Archive leaderboard report
Few-Shot Learning Large COVID-19 CT scan slice dataset CovidExpert Macro F1 0.9872 #1 of 1 Archive leaderboard report
Few-Shot Learning Large COVID-19 CT scan slice dataset CovidExpert Macro Precision 0.9873 #1 of 1 Archive leaderboard report
Few-Shot Learning Large COVID-19 CT scan slice dataset CovidExpert Macro Recall 0.9872 #1 of 1 Archive leaderboard report
Few-Shot Learning Large COVID-19 CT scan slice dataset CovidExpert Micro Precision 0.9872 #1 of 1 Archive leaderboard report
Few-Shot Learning Large COVID-19 CT scan slice dataset CovidExpert Specificity 0.9936 #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

Deep EnsemblesSiamese Network

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