DTF-STCANet: A Dual Time–Frequency Swin Transformer and ConvNeXt Attention Network for Heart Sound Classification

Background/Objectives: Cardiovascular diseases are the leading cause of death worldwide. Therefore, early diagnosis and treatment of these diseases are of critical importance. Stethoscopes are the easiest and fastest medical devices for the initial diagnosis of cardiovascular diseases. However, interpreting heart sounds requires considerable expertise. The use of artificial intelligence in healthcare for decision support has increased and become popular recently. Methods: The popular 2016 PhysioNet/CinC Challenge dataset, consisting of phonocardiogram (PCG) signals, was used to implement the proposed approach. Spectrogram and continuous wavelet transform (CWT) images of the PCG signals were first generated. This increased the distinguishability of the data in terms of both time and frequency components. These two-input images were tested on the developed Dual Time–Frequency Swin Transformer–ConvNeXt Attention Network (DTF-STCANet) model. To further improve classification accuracy, the Weighted KNN algorithm was preferred during the classification phase. Results: With the proposed approach, a 99.29% classification accuracy was achieved. Performance was compared with other state-of-the-art models. Conclusions: The proposed approach, through the integration of PCG signals with artificial intelligence, further strengthens the concept of early diagnosis of heart disease.