Respiratory illnesses like COPD and asthma offer global health implications. Early detection is vital yet problematic due to limited diagnostic technologies and manual assessment by healthcare professionals. This project addresses the need for a machine learning based system to detect respiratory problems through analyzing respiratory sounds. An ensemble model utilizing feature CNNs was created, including audio preprocessing approaches such as noise handling and segmentation to preprocess breathing sound data for analysis. Each feature CNN consisted of eight layers. Features like Mel-Frequency Cepstral Coefficients (MFCC), Chroma, and Mel-Spectrogram were retrieved to facilitate disease classification. The model was subjected to hyperparameter optimization and cross-validation to enhance its performance. Preliminary testing shows promising results, with the model achieving an accuracy of 86% and sensitivity of 88% on a diverse dataset of lung sounds. These results indicate the model’s potential in supporting early disease detection, even outside clinical settings.
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