Interpretable arrhythmia classification using a convolutional neural network and the LIME technique
Deep learning models have demonstrated strong performance in electrocardiogram (ECG) arrhythmia classification. However, their lack of interpretability limits clinical trust and adoption. By adopting an explainable artificial intelligence (XAI) technique, this study aims to enhance the interpretabil...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Universiti Teknologi MARA, Perak
2025
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/128980/1/128980.pdf https://doi.org/10.24191/mij.v6i2.9317 https://ir.uitm.edu.my/id/eprint/128980/ https://mijuitm.com.my/ |
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