# Diagnosis of COVID-19 disease using CT scan images and pre-trained models

arXiv: https://arxiv.org/abs/2208.07829 · HTML: https://mlsanity.com/paper/2208.07829/

## 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.

## Builds on (most cited first, 7 of 7)

- [Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learning](https://mlsanity.com/paper/2004.09363/) (2020)
- [COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images](https://mlsanity.com/paper/2003.11055/) (2020)
- [Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network](https://mlsanity.com/paper/2003.13815/) (2020)
- [Contrastive Cross-site Learning with Redesigned Net for COVID-19 CT Classification](https://mlsanity.com/paper/2009.07652/) (2020)
- [CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Image](https://mlsanity.com/paper/2005.03059/) (2020)
- [COVID CT-Net: Predicting Covid-19 From Chest CT Images Using Attentional Convolutional Network](https://mlsanity.com/paper/2009.05096/) (2020)
- [Deep Convolutional Neural Networks to Diagnose COVID-19 and other Pneumonia Diseases from Posteroanterior Chest X-Rays](https://mlsanity.com/paper/2005.00845/) (2020)

## 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.