# Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning

arXiv: https://arxiv.org/abs/2009.10474 · HTML: https://mlsanity.com/paper/2009.10474/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2020.

## Built on by (most cited first, 3 of 3)

- [UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection](https://mlsanity.com/paper/2105.08590/) (2021)
- [Res-Dense Net for 3D Covid Chest CT-scan classification](https://mlsanity.com/paper/2208.04613/) (2022)
- [A Comprehensive Comparison of Deep Learning Architectures for COVID-19 Classification on CT & X-ray Imagery](https://mlsanity.com/paper/2605.20445/) (2026)

## Abstract (arXiv)

Since December of 2019, novel coronavirus disease COVID-19 has spread around the world infecting millions of people and upending the global economy. One of the driving reasons behind its high rate of infection is due to the unreliability and lack of RT-PCR testing. At times the turnaround results span as long as a couple of days, only to yield a roughly 70% sensitivity rate. As an alternative, recent research has investigated the use of Computer Vision with Convolutional Neural Networks (CNNs) for the classification of COVID-19 from CT scans. Due to an inherent lack of available COVID-19 CT data, these research efforts have been forced to leverage the use of Transfer Learning. This commonly employed Deep Learning technique has shown to improve model performance on tasks with relatively small amounts of data, as long as the Source feature space somewhat resembles the Target feature space. Unfortunately, a lack of similarity is often encountered in the classification of medical images as publicly available Source datasets usually lack the visual features found in medical images. In this study, we propose the use of Multi-Source Transfer Learning (MSTL) to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans. With our multi-source fine-tuning approach, our models outperformed baseline models fine-tuned with ImageNet. We additionally, propose an unsupervised label creation process, which enhances the performance of our Deep Residual Networks. Our best performing model was able to achieve an accuracy of 0.893 and a Recall score of 0.897, outperforming its baseline Recall score by 9.3%.