# Explainable AI For COVID-19 CT Classifiers: An Initial Comparison Study

arXiv: https://arxiv.org/abs/2104.14506 · HTML: https://mlsanity.com/paper/2104.14506/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 25 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans](https://mlsanity.com/paper/2008.06388/) (2020)
- [Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond](https://mlsanity.com/paper/2102.01998/) (2021)
- [Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images](https://mlsanity.com/paper/2004.06689/) (2020)
- [Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia](https://mlsanity.com/paper/2005.02690/) (2020)

## Built on by (most cited first, 11 of 11)

- [Robust Weakly Supervised Learning for COVID-19 Recognition Using Multi-Center CT Images](https://mlsanity.com/paper/2112.04984/) (2021)
- [Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review](https://mlsanity.com/paper/2112.12705/) (2021)
- [Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels](https://mlsanity.com/paper/2202.07422/) (2022)
- [An Explainable-AI approach for Diagnosis of COVID-19 using MALDI-ToF Mass Spectrometry](https://mlsanity.com/paper/2109.14099/) (2021)
- [Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives](https://mlsanity.com/paper/2105.01800/) (2021)
- [Multiple Sclerosis Lesions Segmentation using Attention-Based CNNs in FLAIR Images](https://mlsanity.com/paper/2201.01832/) (2022)
- [Interpretable Bangla Sarcasm Detection using BERT and Explainable AI](https://mlsanity.com/paper/2303.12772/) (2023)
- [A Brief Review of Explainable Artificial Intelligence in Healthcare](https://mlsanity.com/paper/2304.01543/) (2023)
- [CT-xCOV: a CT-scan based Explainable Framework for COVid-19 diagnosis](https://mlsanity.com/paper/2311.14462/) (2023)
- [Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Survey](https://mlsanity.com/paper/2507.07148/) (2025)
- [Explainable AI (XAI) in Biomedical Signal and Image Processing: Promises and Challenges](https://mlsanity.com/paper/2207.04295/) (2022)

## Abstract (arXiv)

Artificial Intelligence (AI) has made leapfrogs in development across all the industrial sectors especially when deep learning has been introduced. Deep learning helps to learn the behaviour of an entity through methods of recognising and interpreting patterns. Despite its limitless potential, the mystery is how deep learning algorithms make a decision in the first place. Explainable AI (XAI) is the key to unlocking AI and the black-box for deep learning. XAI is an AI model that is programmed to explain its goals, logic, and decision making so that the end users can understand. The end users can be domain experts, regulatory agencies, managers and executive board members, data scientists, users that use AI, with or without awareness, or someone who is affected by the decisions of an AI model. Chest CT has emerged as a valuable tool for the clinical diagnostic and treatment management of the lung diseases associated with COVID-19. AI can support rapid evaluation of CT scans to differentiate COVID-19 findings from other lung diseases. However, how these AI tools or deep learning algorithms reach such a decision and which are the most influential features derived from these neural networks with typically deep layers are not clear. The aim of this study is to propose and develop XAI strategies for COVID-19 classification models with an investigation of comparison. The results demonstrate promising quantification and qualitative visualisations that can further enhance the clinician's understanding and decision making with more granular information from the results given by the learned XAI models.