Diagnosis of COVID-19 disease using CT scan images and pre-trained models
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Abstract (arXiv)
Diagnosis of COVID-19 is necessary to prevent and control the disease. Deep learning methods have been considered a fast and accurate method. In this paper, by the parallel combination of three well-known pre-trained networks, we attempted to distinguish coronavirus-infected samples from healthy samples. The negative log-likelihood loss function has been used for model training. CT scan images in the SARS-CoV-2 dataset were used for diagnosis. The SARS-CoV-2 dataset contains 2482 images of lung CT scans, of which 1252 images belong to COVID-19-infected samples. The proposed model was close to 97% accurate.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 25 references that are in the map. First posted on arXiv 16 Aug 2022.
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7 of 25 references.
References · 7
- 2020Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learningcited by 67
- 2020COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Imagescited by 60
- 2020Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural networkcited by 43
- 2020Contrastive Cross-site Learning with Redesigned Net for COVID-19 CT Classificationcited by 22
- 2020CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Imagecited by 8
- 2020COVID CT-Net: Predicting Covid-19 From Chest CT Images Using Attentional Convolutional Networkcited by 7
- 2020Deep Convolutional Neural Networks to Diagnose COVID-19 and other Pneumonia Diseases from Posteroanterior Chest X-Rayscited by 2
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