Cardiovascular diseases (CVDs) remain a leading cause of global mortality, underscoring the need for accurate and efficient diagnostic tools. This study presents an enhanced heart sound classification framework based on a Convolutional Neural Network (CNN) integrated with the Convolutional Block Attention Module (CBAM). Heart sound recordings from the PhysioNet CinC 2016 dataset were segmented and transformed into spectrograms, and twelve CNN models with varying CBAM configurations were systematically evaluated. Experimental results demonstrate that selectively integrating CBAM into early and mid-level convolutional blocks significantly improves classification performance. The optimal model, with CBAM applied after Conv Blocks 1-1, 1-2, and 2-1, achieved an accuracy of 98.66%, outperforming existing state-of-the-art methods. Additional validation using an independent test set from the PhysioNet 2022 database confirmed the model's generalization capability, achieving an accuracy of 95.6% and an AUC of 96.29%. Furthermore, T-SNE visualizations revealed clear class separation, highlighting the model's ability to extract highly discriminative features. These findings confirm the efficacy of attention-based architectures in medical signal classification and support their potential for real-world clinical applications.