# Radiologist-Level COVID-19 Detection Using CT Scans with Detail-Oriented Capsule Networks

arXiv: https://arxiv.org/abs/2004.07407 · HTML: https://mlsanity.com/paper/2004.07407/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2020.

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

- [Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans](https://mlsanity.com/paper/2101.05442/) (2021)
- [PrepNet: A Convolutional Auto-Encoder to Homogenize CT Scans for Cross-Dataset Medical Image Analysis](https://mlsanity.com/paper/2208.09408/) (2022)
- [A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging](https://mlsanity.com/paper/2108.04344/) (2021)
- [CapsNet for Medical Image Segmentation](https://mlsanity.com/paper/2203.08948/) (2022)
- [Cross-dataset domain adaptation for the classification COVID-19 using chest computed tomography images](https://mlsanity.com/paper/2311.08524/) (2023)

## Abstract (arXiv)

Radiographic images offer an alternative method for the rapid screening and monitoring of Coronavirus Disease 2019 (COVID-19) patients. This approach is limited by the shortage of radiology experts who can provide a timely interpretation of these images. Motivated by this challenge, our paper proposes a novel learning architecture, called Detail-Oriented Capsule Networks (DECAPS), for the automatic diagnosis of COVID-19 from Computed Tomography (CT) scans. Our network combines the strength of Capsule Networks with several architecture improvements meant to boost classification accuracies. First, DECAPS uses an Inverted Dynamic Routing mechanism which increases model stability by preventing the passage of information from non-descriptive regions. Second, DECAPS employs a Peekaboo training procedure which uses a two-stage patch crop and drop strategy to encourage the network to generate activation maps for every target concept. The network then uses the activation maps to focus on regions of interest and combines both coarse and fine-grained representations of the data. Finally, we use a data augmentation method based on conditional generative adversarial networks to deal with the issue of data scarcity. Our model achieves 84.3% precision, 91.5% recall, and 96.1% area under the ROC curve, significantly outperforming state-of-the-art methods. We compare the performance of the DECAPS model with three experienced, well-trained thoracic radiologists and show that the architecture significantly outperforms them. While further studies on larger datasets are required to confirm this finding, our results imply that architectures like DECAPS can be used to assist radiologists in the CT scan mediated diagnosis of COVID-19.