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Related Experiment Video

Updated: May 9, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

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.

Keywords:

ResNet18 associated diseases attention mechanism cough classification deep learning

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Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

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Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

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.