Predicting Respiratory Diseases from audio base input of coughing and sneezes.
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Updated
Feb 21, 2024 - Jupyter Notebook
Predicting Respiratory Diseases from audio base input of coughing and sneezes.
Smart, portable spirometer with real-time lung function analysis and mobile app integration for accessible respiratory health monitoring.
MPH Capstone Data Management & Analysis
Lung Sound Classification Models
A Python-based dataset of high-quality respiratory sound recordings, annotated for machine learning tasks focused on detecting lung conditions like wheezes and crackles. It includes preprocessed audio, annotations, and subject metadata for research in respiratory health.
A full-stack deep learning application that analyzes lung or breath sounds to detect diseases like asthma, COPD, and pneumonia using spectrograms and a trained CNN model. Built using Flask, TensorFlow, and Librosa
Code to accompany "Use of Patient-Reported Symptom Data in Clinical Decision Rules for Predicting Influenza in a Telemedicine Setting" by Billings et al, JABFM 2023
AI-powered respiratory disease detection using deep learning on audio spectrograms | Published IEEE Research | Multi-class classification of 8 respiratory conditions including COPD, Asthma, and Pneumonia
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