Improvise approach for respiratory pathologies classification with multilayer convolutional neural networks.

Area of Science:

• Biomedical Engineering
• Artificial Intelligence in Medicine
• Respiratory Medicine

Background:

• Respiratory health assessments rely on analyzing lung sound audio, a process that is time-consuming and requires expertise.
• Accurate diagnosis of lung pathologies from audio data is crucial for effective patient management.

Purpose of the Study:

• To develop a deep learning-based system for automated diagnosis of lung pathologies from respiratory sounds.
• To improve the efficiency and accuracy of lung sound analysis using advanced signal processing and Convolutional Neural Networks (CNNs).

Main Methods:

• Utilized the International Conference on Biomedical and Health Informatics (ICBHI) corpus dataset for lung sound analysis.
• Implemented a novel approach involving pre-processing lung sound audio using Mel-Frequency Cepstral Coefficients (MFCC), Melspectrogram, and Chroma CENS features.
• Developed and applied a newly proposed Convolutional Neural Network (CNN) architecture for classification.

Main Results:

• The proposed system, combining MFCC, Melspectrogram, Chroma CENS, and CNN, demonstrated improved performance in diagnosing lung pathologies.
• Comparative analysis indicated superior performance over existing state-of-the-art methods.
• The system successfully classified respiratory pathologies even in the absence of characteristic wheeze or crackle sounds.

Conclusions:

• The novel deep learning approach offers an effective and accurate method for automated lung sound analysis and respiratory pathology diagnosis.
• This method enhances diagnostic capabilities by not relying on the presence of specific adventitious lung sounds.
• The findings suggest a significant advancement in leveraging AI for respiratory health monitoring.