In recent years, deep learning has shown promise in automating heart-sound classification. Although this approach is fast, non-invasive, and cost-effective, its diagnostic accuracy still mainly depends on the clinician’s expertise, making it particularly challenging to detect rare or complex conditions. This study is motivated by two key concerns in the field of heart-sound classification. First, we observed that automatic heart-sound segmentation algorithms—commonly used for data augmentation—produce varying outcomes, raising concerns about the accuracy of both the segmentation process and the resulting classification performance. Second, we noticed inconsistent accuracy scores across different pretrained models, prompting the need for interpretable explanations to validate these results. We argue that without interpretability to support reported metrics, accuracy scores can be misleading because of ambiguity in how training data interact with pretrained models. Specifically, it remains unclear whether these models classify spectrogram images—generated from heart-sound signals—in a way that aligns with clinical reasoning, where experts focus on specific components of the heart cyc
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