Lightweight Hybrid Deep Learning Models for Accurate Classification of Respiratory Conditions from Raw Lung Sounds.

In recent years, progress in artificial intelligence, particularly in the realm of deep learning, has resulted in substantial enhancements in the diagnosis of various medical conditions. This study introduces a framework that leverages multiple lightweight deep learning models to assess their effectiveness in analyzing raw lung auscultation sounds - no feature engineering or preprocessing - to detect eleven different respiratory pathologies. The objective was to enhance the accuracy of respiratory disease diagnoses and conduct a comparative analysis of these models to pinpoint the most efficient model. The models were assessed based on their performance across two distinct datasets, one in its original form and the other after augmentation. The outcomes underscore the successful utilization of the deep learning framework, because it achieves remarkable accuracy in the detection of respiratory pathologies through the analysis of raw lung sounds alone. Furthermore, all the deep learning models proposed in the framework exhibited accuracy rates exceeding 99%, with the hybrid convolutional neural network (CNN)-long short-term memory (LSTM) model, which combines CNN for feature extraction and LSTM for temporal modeling, emerging as the top performer across all datasets. The augmentation process was also proven to be effective, leading to performance enhancements in deep-learning models. Finally, the lightweight hybrid CNN-LSTM model, which is less complex with only 15 layers, outperformed the standalone CNN and LSTM architectures, achieving up to 100% accuracy on the augmented dataset. These results suggest that raw auscultation sounds can be used to reliably detect multiple respiratory pathologies using lightweight and deployable deep learning models. The reported performance metrics reflect in-dataset evaluation only, and external validation on data from additional clinical datasets will be required to assess generalization.