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Deep Learning for Arrhythmia Detection by Prem Swaroop is a document available to read on EtoBox.

What is Deep Learning for Arrhythmia Detection about?

The document presents a comparative study on deep learning models for classifying arrhythmias using ECG images, highlighting the effectiveness of Custom CNN, VGG19, and Inception V3 models. The VGG19 model achieved a training accuracy of 95.7% and testing accuracy of 93.8%, demonstrating its potential for automating arrhythmia detection and improving diagnostic accuracy. The research emphasizes the importance of deep learning in enhancing real-time monitoring and diagnosis of heart conditions.

Author

Prem Swaroop

Language

EN