Enhanced Lung Disease Classification Using CALMNet: A Hybrid CNN-LSTM-TimeDistributed Model for Respiratory Sound Analysis

Lung diseases, such as asthma, pneumonia, and chronic obstructive pulmonary disease (COPD), pose considerable global health issues, making early diagnosis essential for effective treatment. Traditional diagnostic techniques for these ailments, especially auscultation using a stethoscope, are subjective and susceptible to inaccuracies. This study introduces CALMNet, a deep learning model that uses Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and a TimeDistributed layer to accurately analyze lung sounds by considering both their spatial and time-related features. The model is trained and assessed utilizing the ICBHI dataset, which encompasses a varied collection of lung sounds from patients with various respiratory ailments. Preprocessing techniques, including double denoising (FFT and high-pass filtering), were utilized to enhance the audio data quality, while feature extraction methods such as Mel Spectrograms, Mel-Frequency Cepstral Coefficients (MFCCs), and Chroma Short-Time Fourier Transform (ChromaSTFT) were employed to capture the fundamental characteristics of lung sounds. The results show that CALMNet achieves an impressive accuracy of 97.65%, with an F1-score of 0.909, precision of 0.911, and recall of 0.90, outperforming other models like CNNs and LSTMs. CALMNet exhibits enhanced efficacy in differentiating various lung illnesses, as evidenced by the confusion matrix and ROC curve analysis. The model’s exceptional accuracy and strong performance indicate its promise as an effective instrument for the automated classification of lung illnesses, providing a dependable, objective, and scalable option for clinical use. Future endeavors will concentrate on augmenting the dataset, enhancing model interpretability, and executing real-time diagnostic applications to further elevate its applicability in healthcare environments.