# Transfer Learning to Detect COVID-19 Coughs with Incremental Addition of Patient Coughs to Healthy People's Cough Detection Models

arXiv: https://arxiv.org/abs/2311.06707 · HTML: https://mlsanity.com/paper/2311.06707/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 74 references that are in the map.
- First posted on arXiv 12 Nov 2023.

## Builds on (most cited first, 16 of 16)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms](https://mlsanity.com/paper/2009.11644/) (2020)
- [Coswara -- A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis](https://mlsanity.com/paper/2005.10548/) (2020)
- [AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an App](https://mlsanity.com/paper/2004.01275/) (2020)
- [COVID-19 Cough Classification using Machine Learning and Global Smartphone Recordings](https://mlsanity.com/paper/2012.01926/) (2020)
- [DeepCough: A Deep Convolutional Neural Network in A Wearable Cough Detection System](https://mlsanity.com/paper/1509.02512/) (2015)
- [Objective Study of Sensor Relevance for Automatic Cough Detection](https://mlsanity.com/paper/2001.00537/) (2019)
- [LCANets++: Robust Audio Classification using Multi-layer Neural Networks with Lateral Competition](https://mlsanity.com/paper/2308.12882/) (2023)
- [Adherence to Personal Health Devices: A Case Study in Diabetes Management](https://mlsanity.com/paper/2006.04947/) (2020)
- [Continuous Authentication of Wearable Device Users from Heart Rate, Gait, and Breathing Data](https://mlsanity.com/paper/2008.10779/) (2020)
- [Biometric-Based Wearable User Authentication During Sedentary and Non-sedentary Periods](https://mlsanity.com/paper/1811.07060/) (2018)
- [Summary: Multi-modal Biometric-based Implicit Authentication of Wearable Device Users](https://mlsanity.com/paper/1907.06563/) (2019)
- [Context-Dependent Implicit Authentication for Wearable Device User](https://mlsanity.com/paper/2008.12145/) (2020)
- [Understanding College Students' Phone Call Behaviors Towards a Sustainable Mobile Health and Wellbeing Solution](https://mlsanity.com/paper/2011.06007/) (2020)
- [Opportunistic Multi-Modal User Authentication for Health-Tracking IoT Wearables](https://mlsanity.com/paper/2109.13705/) (2021)
- [Discovering COVID-19 Coughing and Breathing Patterns from Unlabeled Data Using Contrastive Learning with Varying Pre-Training Domains](https://mlsanity.com/paper/2306.01864/) (2023)

## Built on by (most cited first, 5 of 5)

- [Cough Classification using Few-Shot Learning](https://mlsanity.com/paper/2509.09515/) (2025)
- [Mitigating Sex Bias in Audio Data-driven COPD and COVID-19 Breathing Pattern Detection Models](https://mlsanity.com/paper/2409.10677/) (2024)
- [Toward Mitigating Sex Bias in Pilot Trainees' Stress and Fatigue Modeling](https://mlsanity.com/paper/2409.10676/) (2024)
- [Sound Classification of Four Insect Classes](https://mlsanity.com/paper/2412.12395/) (2024)
- [A Comprehensive Survey of Challenges and Opportunities of Few-Shot Learning Across Multiple Domains](https://mlsanity.com/paper/2504.04017/) (2025)

## Abstract (arXiv)

Millions of people have died worldwide from COVID-19. In addition to its high death toll, COVID-19 has led to unbearable suffering for individuals and a huge global burden to the healthcare sector. Therefore, researchers have been trying to develop tools to detect symptoms of this human-transmissible disease remotely to control its rapid spread. Coughing is one of the common symptoms that researchers have been trying to detect objectively from smartphone microphone-sensing. While most of the approaches to detect and track cough symptoms rely on machine learning models developed from a large amount of patient data, this is not possible at the early stage of an outbreak. In this work, we present an incremental transfer learning approach that leverages the relationship between healthy peoples' coughs and COVID-19 patients' coughs to detect COVID-19 coughs with reasonable accuracy using a pre-trained healthy cough detection model and a relatively small set of patient coughs, reducing the need for large patient dataset to train the model. This type of model can be a game changer in detecting the onset of a novel respiratory virus.