VoxMed: One-Step Respiratory Disease Classifier using Digital Stethoscope Sounds

TL;DR

VoxMed is a real-time diagnostic tool that uses an Audio Spectrogram Transformer and 1-D CNN to classify respiratory diseases from digital stethoscope sounds, aiding quick clinical decisions.

Contribution

It introduces a novel UI-assisted one-step classifier combining AST and CNN for respiratory disease diagnosis from stethoscope recordings.

Findings

Achieved high accuracy on the ICBHI dataset.

Provides rapid diagnosis within seconds.

Accessible via GitHub for clinical use.

Abstract

As respiratory illnesses become more common, it is crucial to quickly and accurately detect them to improve patient care. There is a need for improved diagnostic methods for immediate medical assessments for optimal patient outcomes. This paper introduces VoxMed, a UI-assisted one-step classifier that uses digital stethoscope recordings to diagnose respiratory diseases. It employs an Audio Spectrogram Transformer(AST) for feature extraction and a 1-D CNN-based architecture to classify respiratory diseases, offering professionals information regarding their patients respiratory health in seconds. We use the ICBHI dataset, which includes stethoscope recordings collected from patients in Greece and Portugal, to classify respiratory diseases. GitHub repository: https://github.com/Sample-User131001/VoxMed