Abstract: A heart attack disrupts the normal flow of blood to the heart muscle, potentially causing severe damage or death if not treated promptly. It can lead to long-term health complications, reduce quality of life, and significantly impact daily activities and overall well-being. Despite the growing popularity of deep learning, several drawbacks persist, such as complexity and the limitation of single-model learning. In this paper, we introduce a residual learning-based feature fusion technique to achieve high accuracy in differentiating abnormal cardiac rhythms heart sound. Combining MobileNet with DenseNet201 for feature fusion leverages MobileNet lightweight, efficient architecture with DenseNet201, dense connections, resulting in enhanced feature extraction and improved model performance with reduced computational cost. To further enhance the fusion, we employed residual learning to optimize the hierarchical features of heart abnormal sounds during training. The experimental results demonstrate that the proposed fusion method achieved an accuracy of 95.67% on the benchmark PhysioNet-2016 Spectrogram dataset. To further validate the performance, we applied it to the BreakHis dataset with a magnification level of 100X. The results indicate that the model maintains robust performance on the second dataset, achieving an accuracy of 96.55%. it highlights its consistent performance, making it a suitable for various applications.

Key words: Cardiac rhythms')">Cardiac rhythms, Feature fusion, Residual learning, BreakHis, Spectrogram sound