Cardio-Dense: Diagnosis of Cardiac Abnormalities Based on Phonocardiogram Using Improved Swin Transformer Through Lightweight Dense Blocks

Background: Cardiovascular diseases (CVDs) are among the top sources of mortality worldwide. To properly diagnose cardiovascular diseases, a low-cost remedy based on phonocardiography (PCG) signals must be proposed. Several deep learning (DL)-driven CVD systems are now being developed to identify various phases of the disease. Nevertheless, the approaches’ accuracy falls short of expectations, and they necessitate substantial processing resources and training data. Methods: This paper proposes Cardio-Dense, a hybrid framework for multi-class CVD detection from phonocardiogram signals. The PCG waveform is first denoised in the wavelet domain and then converted into a 2D time–frequency spectrogram using continuous wavelet transform (CWT). We design a joint architecture that combines a Swin transformer for capturing global contextual dependencies with lightweight DenseBlocks for efficient local feature refinement, enabling robust learning from PCG spectrograms across five disease classes. Results: Experiments on PCG datasets achieve up to 0.977 accuracy, 0.975 sensitivity, 0.992 specificity, 0.978 F1-score, 0.978 AUC, and 0.976 precision, while maintaining low computational overhead suitable for real-time inference. Conclusions: The findings indicate that the proposed model provides an economical, non-invasive method for preliminary signal-level identification of multi-class heart valve diseases. It benefits clinicians by decreasing the need for arduous and error-prone manual PCG analysis. Furthermore, it offers quick, near-real-time categorization suitable for clinical and portable applications.