victor369basu/Respiratory-diseases-recognition-through-respiratory-sound-with-the-help-of-deep-neural-network

Prediction of respiratory diseases such as COPD(Chronic obstructive pulmonary disease), URTI(upper respiratory tract infection), Bronchiectasis, Pneumonia, Bronchiolitis with the help of deep neural networks or deep learning. We have constructed a deep neural network model that takes in respiratory sound as input and classifies the condition of its respiratory system. It not only classifies among the above-mentioned disease but also classifies if a person’s respiratory system is healthy or not with higher accuracy and precision.

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The project employs Mel-frequency cepstral coefficients (MFCCs) for spectral feature extraction from raw audio, feeding 40-dimensional feature vectors into a deep neural network trained with Categorical Cross-entropy loss and Adamax optimization. It addresses class imbalance through data augmentation techniques including random noise injection, time-shifting, and time-stretching via librosa, evaluated across six balanced respiratory disease categories using comprehensive metrics (accuracy, precision, recall, F1-score, Cohen's kappa, and Matthews correlation coefficient).

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Jun 25, 2021

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