# Custom Deep Neural Network for 3D Covid Chest CT-scan Classification

arXiv: https://arxiv.org/abs/2107.01456 · HTML: https://mlsanity.com/paper/2107.01456/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 7 references that are in the map.
- First posted on arXiv 3 Jul 2021.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [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)
- [Macro F1 and Macro F1](https://mlsanity.com/paper/1911.03347/) (2019)

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

- [Deep Learning Based Automated COVID-19 Classification from Computed Tomography Images](https://mlsanity.com/paper/2111.11191/) (2021)

## Abstract (arXiv)

3D CT-scan base on chest is one of the controversial topisc of the researcher nowadays. There are many tasks to diagnose the disease through CT-scan images, include Covid19. In this paper, we propose a method that custom and combine Deep Neural Network to classify the series of 3D CT-scans chest images. In our methods, we experiment with 2 backbones is DenseNet 121 and ResNet 101. In this proposal, we separate the experiment into 2 tasks, one is for 2 backbones combination of ResNet and DenseNet, one is for DenseNet backbones combination.