Classification of Respiratory Conditions using Auscultation Sound.

Area of Science:

• Computational intelligence
• Medical informatics
• Respiratory medicine

Background:

• Effective management of respiratory conditions necessitates accurate and timely diagnosis.
• Automated analysis of breath sounds offers potential for enhanced diagnostic reliability and accuracy.
• Digital health solutions enable remote monitoring and personalized patient self-management.

Purpose of the Study:

• To develop and compare deep learning models for automatic classification of respiratory conditions.
• To differentiate between healthy individuals, patients with Chronic Obstructive Pulmonary Disease (COPD), and patients with pneumonia using sound recognition.
• To evaluate the efficacy of Multi-layer Perceptron Classifier (MLPClassifier) and Convolutional Neural Networks (CNN) for this diagnostic task.

Main Methods:

• Utilized deep learning techniques, specifically MLPClassifier and CNN, for sound recognition.
• Trained and tested models on a dataset of respiratory sounds from healthy individuals and patients with COPD and pneumonia.
• Compared the performance of the developed sound recognition models in classifying the distinct respiratory conditions.

Main Results:

• Deep learning models demonstrated capability in differentiating between healthy subjects and patients with specific respiratory diseases.
• CNN and MLPClassifier showed potential for accurate classification of respiratory sounds.
• The study provides a comparative analysis of these AI approaches for medical sound analysis.

Conclusions:

• Automated classification of breath sounds using deep learning holds significant potential for improving respiratory diagnostics.
• These models can support healthcare providers in early diagnosis and remote patient monitoring.
• Further research in AI-driven auscultation analysis can advance personalized respiratory care and self-management strategies.