{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/covidexpert-a-triplet-siamese-neural-network","title":"CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-19","arxiv_id":"2302.09004","date":"2023-02-17","proceeding":null,"authors":["Tareque Rahman Ornob","Gourab Roy","Enamul Hassan"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2302.09004v1","url_pdf":"https://arxiv.org/pdf/2302.09004v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"covid-19-detection","task_name":"COVID-19 Diagnosis"},{"task_slug":"computed-tomography-ct","task_name":"Computed Tomography (CT)"},{"task_slug":"few-shot-image-classification","task_name":"Few-Shot Image Classification"},{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":null,"task_name":"Triplet"}],"methods":[{"method_slug":"deep-ensembles","method_name":"Deep Ensembles"},{"method_slug":"siamese-network","method_name":"Siamese Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/covid-19-diagnosis-on-large-covid-19-ct-scan","task":"COVID-19 Diagnosis","dataset":"Large COVID-19 CT scan slice dataset","model":"CovidExpert","rank_in_archive_order":1,"of":1,"metrics":{"AUC-ROC":"0.9992","Accuracy":"0.98719","Macro F1":"0.9872","Macro Precision":"0.9873","Macro Recall":"0.9872","Micro Precision":"0.9872","Specificity":"0.9936"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-learning-on-large-covid-19-ct-scan","task":"Few-Shot Learning","dataset":"Large COVID-19 CT scan slice dataset","model":"CovidExpert","rank_in_archive_order":1,"of":1,"metrics":{"AUC-ROC":"0.9992","Accuracy ":"0.98719","Macro F1":"0.9872","Macro Precision":"0.9873","Macro Recall":"0.9872","Micro Precision":"0.9872","Specificity":"0.9936"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}