Abstract :

Cardiovascular Disease, Especially Heart Problems, Represent A Significant Global Mortality Factor, Which Is An Early Prognosis. Economic And Non -invasive Tool, Electrocardiogram (ECG), Allows Identity Of Numerous Diseases Via Gazing Heart Interest. To Enhance Predictive Accuracy, Deep Mastering Method Is Used To Hit Upon 4 Important Coronary Heart Anomalies: Arrhythmia, Myocardial Infarction, Myocardial And Everyday Instance Infarction. Research Integrates Transmission Getting To Know Of From DNN Including Squeezenet And Alexnet, With CNN Structure. This Method Is Designed To Extract Huge Characteristics And Increase Predictions In Aggregate With Conventional Tool DL Techniques. The Proposed Model Is Outstanding Through Presenting Top Notch Performance, Which Appreciably Will Increase The Prediction Of Clinical Diseases From Pix. It Emphasizes The Crucial Function Of Synthetic Intelligence In The Transformation Of Fitness Tactics. The Xception Included Version Will Increase The Extraction Of Factors To Detect Heart Abnormalities In ECG Images. Extracted Functions Act As Inputs For Device Learning Fashions And Growth Their Ability To Identify Complex Formulation And Abnormalities. The Integration Of State-of-the-art Extraction Of Elements With Durable System Mastering Strategies Will Increase The Project Efficiency In Presenting Accurate Diagnoses. Optimized User Interactions Using A SQLite Flask Emphasize The Practicality Of The System, Provide Safe Registration, Signin And Effective Testing For Improved Health Care Procedures. Index Terms - Cardiovascular, Deep Learning, Electrocardiogram (ECG) Images, Feature Extraction, Machine Learning, Transfer Learning

Published:

24-9-2025

Issue:

Vol. 25 No. 9 (2025)

Page Nos:

179-193

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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