Why the study?
Heart disease is a leading cause of global mortality, and traditional manual ECG interpretation is time-consuming and error-prone, driving the need for intelligent automated diagnostic systems.
Does a deep learning-based framework utilizing CNNs improve the classification accuracy of ECG images for multiple cardiac conditions?
Does a deep learning-based framework utilizing CNNs improve the classification accuracy of ECG images for multiple cardiac conditions?
A deep learning framework using the DenseNet169 architecture achieved 82% accuracy in classifying ECG images across five diagnostic categories, demonstrating potential as an automated diagnostic support tool.
No takes yet. Share an insight, caveat, or question.
Supports DenseNet169 for automated multi-class ECG classification; extends CNN benchmarks with RCT-level validation.
Chandika et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: