# Coronavirus Detection and Analysis on Chest CT with Deep Learning

arXiv: https://arxiv.org/abs/2004.02640 · HTML: https://mlsanity.com/paper/2004.02640/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging](https://mlsanity.com/paper/2108.04344/) (2021)
- [COVID-19 Detection Based on Blood Test Parameters using Various Artificial Intelligence Methods](https://mlsanity.com/paper/2404.02348/) (2024)
- [COVID-19 Disease Identification on Chest-CT images using CNN and VGG16](https://mlsanity.com/paper/2207.04212/) (2022)
- [A review of Deep learning Techniques for COVID-19 identification on Chest CT images](https://mlsanity.com/paper/2208.00032/) (2022)
- [Cross-dataset domain adaptation for the classification COVID-19 using chest computed tomography images](https://mlsanity.com/paper/2311.08524/) (2023)

## Abstract (arXiv)

The outbreak of the novel coronavirus, officially declared a global pandemic, has a severe impact on our daily lives. As of this writing there are approximately 197,188 confirmed cases of which 80,881 are in "Mainland China" with 7,949 deaths, a mortality rate of 3.4%. In order to support radiologists in this overwhelming challenge, we develop a deep learning based algorithm that can detect, localize and quantify severity of COVID-19 manifestation from chest CT scans. The algorithm is comprised of a pipeline of image processing algorithms which includes lung segmentation, 2D slice classification and fine grain localization. In order to further understand the manifestations of the disease, we perform unsupervised clustering of abnormal slices. We present our results on a dataset comprised of 110 confirmed COVID-19 patients from Zhejiang province, China.