# 3D RegNet: Deep Learning Model for COVID-19 Diagnosis on Chest CT Image

arXiv: https://arxiv.org/abs/2107.04055 · HTML: https://mlsanity.com/paper/2107.04055/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 8 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Designing Network Design Spaces](https://mlsanity.com/paper/2003.13678/) (2020)
- [Deep Transparent Prediction through Latent Representation Analysis](https://mlsanity.com/paper/2009.07044/) (2020)
- [MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis](https://mlsanity.com/paper/2106.07524/) (2021)
- [Conditional Adversarial Network for Semantic Segmentation of Brain Tumor](https://mlsanity.com/paper/1708.05227/) (2017)

## Built on by (most cited first, 1 of 1)

- [Res-Dense Net for 3D Covid Chest CT-scan classification](https://mlsanity.com/paper/2208.04613/) (2022)

## Abstract (arXiv)

In this paper, a 3D-RegNet-based neural network is proposed for diagnosing the physical condition of patients with coronavirus (Covid-19) infection. In the application of clinical medicine, lung CT images are utilized by practitioners to determine whether a patient is infected with coronavirus. However, there are some laybacks can be considered regarding to this diagnostic method, such as time consuming and low accuracy. As a relatively large organ of human body, important spatial features would be lost if the lungs were diagnosed utilizing two dimensional slice image. Therefore, in this paper, a deep learning model with 3D image was designed. The 3D image as input data was comprised of two-dimensional pulmonary image sequence and from which relevant coronavirus infection 3D features were extracted and classified. The results show that the test set of the 3D model, the result: f1 score of 0.8379 and AUC value of 0.8807 have been achieved.