# Interpretability Analysis of Deep Models for COVID-19 Detection

arXiv: https://arxiv.org/abs/2211.14372 · HTML: https://mlsanity.com/paper/2211.14372/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 25 Nov 2022.

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

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/1912.10211/) (2019)
- [Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data](https://mlsanity.com/paper/2006.05919/) (2020)
- [The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates](https://mlsanity.com/paper/2102.13468/) (2021)
- [Exploring Automatic COVID-19 Diagnosis via voice and symptoms from Crowdsourced Data](https://mlsanity.com/paper/2102.05225/) (2021)
- [Mixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network](https://mlsanity.com/paper/1805.07319/) (2018)
- [The voice of COVID-19: Acoustic correlates of infection](https://mlsanity.com/paper/2012.09478/) (2020)

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

- [Acoustic models of Brazilian Portuguese Speech based on Neural Transformers](https://mlsanity.com/paper/2312.09265/) (2023)
- [Discriminant audio properties in deep learning based respiratory insufficiency detection in Brazilian Portuguese](https://mlsanity.com/paper/2405.17569/) (2024)
- [Contrasting Deep Learning Models for Direct Respiratory Insufficiency Detection Versus Blood Oxygen Saturation Estimation](https://mlsanity.com/paper/2407.20989/) (2024)

## Abstract (arXiv)

During the outbreak of COVID-19 pandemic, several research areas joined efforts to mitigate the damages caused by SARS-CoV-2. In this paper we present an interpretability analysis of a convolutional neural network based model for COVID-19 detection in audios. We investigate which features are important for model decision process, investigating spectrograms, F0, F0 standard deviation, sex and age. Following, we analyse model decisions by generating heat maps for the trained models to capture their attention during the decision process. Focusing on a explainable Inteligence Artificial approach, we show that studied models can taken unbiased decisions even in the presence of spurious data in the training set, given the adequate preprocessing steps. Our best model has 94.44% of accuracy in detection, with results indicating that models favors spectrograms for the decision process, particularly, high energy areas in the spectrogram related to prosodic domains, while F0 also leads to efficient COVID-19 detection.