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 and systematic reliability evaluation across four cardiac classes: Abnormal Heartbeat, History of M...
| Authors | Mohammad Sadman Tahsin, Haitham Y. Adarbah, Afzel Noore |
|---|---|
| Primary category | cs.CV |
| Published | 18 September 2026 |
| Last updated at source | 26 September 2026 |
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