Deep Learning-Driven Early Diagnosis of Respiratory Diseases using CNN-RNN Fusion on Lung Sound Data

This research depicts a deep learning-based algorithm designed for lung sound analysis, which combines Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) architectures to improve early disease detection. With comprehensive datasets from Coswara and ICBHI, the algorithm is proficient in distinguishing a spectrum of respiratory diseases, including pneumonia, asthma, and Chronic Obstructive Pulmonary Disease (COPD). Model pre-processing data with high pass filtering and segmented analysis of lung sound recordings, with Mel-spectrograms used as pivotal input features. The complete fusion model architecture integrates three CNN layers, three max-pooling layers, and two fully connected layers, the result is a feature map that highlights the presence of detected features, complemented by including two Long Short-Term Memory (LSTM) layers in the RNN component. The training process is devoted to the Adam optimizer alongside the cross-entropy loss function. Data augmentation techniques were applied to handle class imbalances and enhance model generalizability. The experimental results demonstrate high accuracy, sensitivity, specificity, and F1-score across various respiratory diseases. The performance metrics on the ICBHI dataset underscore the model's exceptional accuracy: 93.3% for healthy individuals, 93.8% for pneumonia patients, 91.7% for asthma patients, and 94.0% for COPD patients. The model outperforms alternative algorithms such as decision trees, support vector machines, and random forest regarding precision, recall, F1 score, and accuracy across the ICBHI and Coswara datasets. This noteworthy outcome positions the algorithm as an advanced and effective solution for progressing the domain of respiratory disease diagnosis through lung sound analysis. The model also provides interpretable visual explanations using Grad-CAM, along with confidence estimates, to enhance clinical trust.