SOURCE-LINKED INTELLIGENCE
X-Beat: An Explainable Framework for ECG Image Classification
Accurate automated interpretation of electrocardio- grams (ECGs) is essential for early detection of cardiac condi- tions such as myocardial infarction and rhythm abnormalities. However, many high-performing deep learning models remain difficult to deploy in clinical settings due to limited transparency and lack of reliability validation. In this work, we present X- Beat, an explainable and reliability-aware benchmark framework for ECG image classification designed to support trustworthy AI systems in healthcare. The proposed framework combines transfer learning with post-hoc explainability an
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-18T22:34:32.000Z
First collected: 2026-09-23T12:01:45.602Z. This is not the publication date.