Paper Title
Comparative Analysis of Deep Learning Models on Ecg Images

Abstract
Electrocardiography (ECG) is an essential tool for diagnosis used to detect and monitor abnormalities in the heart. The aim of this study is to analyze electrocardiogram (ECG) patterns in order to identify abnormalities. Utilizing various deep learning models and assessing their level of precision will help achieve this. The goal is to assess their effectiveness in detecting abnormalities in ECG readings. We assess the performance of each model through a meticulous training and validation procedure. The findings of our study uncover the merits and drawbacks of each model, providing valuable insights into their potential efficacy in practical clinical settings. The results demonstrate that certain models excel over others in particular tasks, underscoring the need of selecting models based on clinical needs. Keywords - Resnet, Deep Learning, ECG, CNN