# Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning

arXiv: https://arxiv.org/abs/2011.05317 · HTML: https://mlsanity.com/paper/2011.05317/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2020.

## Built on by (most cited first, 10 of 10)

- [COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach](https://mlsanity.com/paper/2102.06285/) (2021)
- [Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review](https://mlsanity.com/paper/2112.12705/) (2021)
- [COVID-19 identification from volumetric chest CT scans using a progressively resized 3D-CNN incorporating segmentation, augmentation, and class-rebalancing](https://mlsanity.com/paper/2102.06169/) (2021)
- [Classification of COVID-19 via Homology of CT-SCAN](https://mlsanity.com/paper/2102.10593/) (2021)
- [From Interpretable Filters to Predictions of Convolutional Neural Networks with Explainable Artificial Intelligence](https://mlsanity.com/paper/2207.12958/) (2022)
- [CT-xCOV: a CT-scan based Explainable Framework for COVid-19 diagnosis](https://mlsanity.com/paper/2311.14462/) (2023)
- [Detecting COVID-19 from Chest Computed Tomography Scans using AI-Driven Android Application](https://mlsanity.com/paper/2111.06254/) (2021)
- [Multi-scale alignment and Spatial ROI Module for COVID-19 Diagnosis](https://mlsanity.com/paper/2207.01345/) (2022)
- [Improving COVID-19 CT Classification of CNNs by Learning Parameter-Efficient Representation](https://mlsanity.com/paper/2208.04718/) (2022)
- [MEDNC: Multi-ensemble deep neural network for COVID-19 diagnosis](https://mlsanity.com/paper/2304.13135/) (2023)

## Abstract (arXiv)

This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopt advanced deep network architectures and propose a transfer learning strategy using custom-sized input tailored for each deep architecture to achieve the best performance. We conduct extensive sets of experiments on two CT image datasets, namely the SARS-CoV-2 CT-scan and the COVID19-CT. The obtained results show superior performances for our models compared with previous studies, where our best models achieve average accuracy, precision, sensitivity, specificity and F1 score of 99.4%, 99.6%, 99.8%, 99.6% and 99.4% on the SARS-CoV-2 dataset; and 92.9%, 91.3%, 93.7%, 92.2% and 92.5% on the COVID19-CT dataset, respectively. Furthermore, we apply two visualization techniques to provide visual explanations for the models' predictions. The visualizations show well-separated clusters for CT images of COVID-19 from other lung diseases, and accurate localizations of the COVID-19 associated regions.