Cough Audio Recognition for Early Detection of Respiratory Diseases: Algorithm Development and Validation Study.
Wensheng Sun1, Jiahao Zou1, Na Yin1
1School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, China.
JMIR Medical Informatics
|May 7, 2026
Summary
Analyzing cough sounds with a new AI model (CAM-ResNet18) accurately identifies respiratory diseases. This acoustic analysis offers a promising, non-invasive tool for early diagnosis and improved healthcare efficiency.
Area of Science:
- Artificial Intelligence in Medicine
- Acoustic Signal Processing
- Respiratory Disease Diagnostics
Background:
- Coughing is a key symptom of respiratory illnesses, with unique acoustic properties linked to specific diseases.
- Analyzing cough sound acoustics can aid in rapid identification and preliminary diagnosis of respiratory conditions.
- This technology has potential applications in mobile and ubiquitous health platforms.
Purpose of the Study:
- To investigate the use of acoustic analysis of cough sounds for diagnosing respiratory diseases.
- To enhance diagnostic efficiency for healthcare professionals through cough sound analysis.
Main Methods:
- Collected 2610 voluntary cough audio samples from patients with respiratory diseases (COPD, lung cancer, COVID-19, pneumonia) and healthy individuals.
- Developed a CAM-ResNet18 model by integrating a channel attention mechanism (CAM) into the ResNet18 neural network.
- Converted audio samples to spectrograms for input into the CAM-ResNet18 model for training and classification.
Main Results:
- The CAM-ResNet18 model achieved 83.9% accuracy and an 82.52% average F1-score in classifying 5 types of cough sounds.
- This represents a performance improvement over the traditional ResNet18 model, which had 78.16% accuracy and a 78.29% F1-score.
- The integration of CAM significantly enhanced the model's classification capabilities.
Conclusions:
- The study validates the effectiveness of the CAM-ResNet18 model for cough sound analysis.
- The proposed method shows significant potential for clinical application in diagnosing respiratory diseases.
- Acoustic analysis of coughs offers a novel, efficient diagnostic approach.
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