# CNN Filter Learning from Drawn Markers for the Detection of Suggestive Signs of COVID-19 in CT Images

arXiv: https://arxiv.org/abs/2111.08710 · HTML: https://mlsanity.com/paper/2111.08710/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 16 Nov 2021.

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

- [COVID-CT-Dataset: A CT Scan Dataset about COVID-19](https://mlsanity.com/paper/2003.13865/) (2020)
- [Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19](https://mlsanity.com/paper/2004.02731/) (2020)
- [A Light CNN for detecting COVID-19 from CT scans of the chest](https://mlsanity.com/paper/2004.12837/) (2020)
- [Large-Scale Screening of COVID-19 from Community Acquired Pneumonia using Infection Size-Aware Classification](https://mlsanity.com/paper/2003.09860/) (2020)
- [Learning CNN filters from user-drawn image markers for coconut-tree image classification](https://mlsanity.com/paper/2008.03549/) (2020)

## Built on by (most cited first, 2 of 2)

- [Interactive Image Selection and Training for Brain Tumor Segmentation Network](https://mlsanity.com/paper/2406.03225/) (2024)
- [Building Flyweight FLIM-based CNNs with Adaptive Decoding for Object Detection](https://mlsanity.com/paper/2306.14840/) (2023)

## Abstract (arXiv)

Early detection of COVID-19 is vital to control its spread. Deep learning methods have been presented to detect suggestive signs of COVID-19 from chest CT images. However, due to the novelty of the disease, annotated volumetric data are scarce. Here we propose a method that does not require either large annotated datasets or backpropagation to estimate the filters of a convolutional neural network (CNN). For a few CT images, the user draws markers at representative normal and abnormal regions. The method generates a feature extractor composed of a sequence of convolutional layers, whose kernels are specialized in enhancing regions similar to the marked ones, and the decision layer of our CNN is a support vector machine. As we have no control over the CT image acquisition, we also propose an intensity standardization approach. Our method can achieve mean accuracy and kappa values of 0.97 and 0.93, respectively, on a dataset with 117 CT images extracted from different sites, surpassing its counterpart in all scenarios.