# Automated Detection of COVID-19 from CT Scans Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2006.13212 · HTML: https://mlsanity.com/paper/2006.13212/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2020.

## Built on by (most cited first, 4 of 4)

- [The Prominence of Artificial Intelligence in COVID-19](https://mlsanity.com/paper/2111.09537/) (2021)
- [A Classical-Quantum Convolutional Neural Network for Detecting Pneumonia from Chest Radiographs](https://mlsanity.com/paper/2202.10452/) (2022)
- [COVID-19 Disease Identification on Chest-CT images using CNN and VGG16](https://mlsanity.com/paper/2207.04212/) (2022)
- [Cross-dataset domain adaptation for the classification COVID-19 using chest computed tomography images](https://mlsanity.com/paper/2311.08524/) (2023)

## Abstract (arXiv)

COVID-19 is an infectious disease that causes respiratory problems similar to those caused by SARS-CoV (2003). Currently, swab samples are being used for its diagnosis. The most common testing method used is the RT-PCR method, which has high specificity but variable sensitivity. AI-based detection has the capability to overcome this drawback. In this paper, we propose a prospective method wherein we use chest CT scans to diagnose the patients for COVID-19 pneumonia. We use a set of open-source images, available as individual CT slices, and full CT scans from a private Indian Hospital to train our model. We build a 2D segmentation model using the U-Net architecture, which gives the output by marking out the region of infection. Our model achieves a sensitivity of 96.428% (95% CI: 88%-100%) and a specificity of 88.39% (95% CI: 82%-94%). Additionally, we derive a logic for converting our slice-level predictions to scan-level, which helps us reduce the false positives.