Detection and Classification of Cardiovascular Diseases in ECG Images using Deep Learning

Authors: Nilesh Dagadu Navale, Tripti Arjariya

DOI: https://doi.org/10.37082/IJIRMPS.v13.i4.232631

Short DOI: https://doi.org/g9stkd

Country: India

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Abstract: Cardiovascular diseases (CVDs) are a significant contributor to global mortality, necessitating healthcare systems to provide precise and timely diagnoses for effective interventions. Electrocardiograms (ECGs) are widely employed for detecting and diagnosing heart conditions; however, relying solely on ECG images may not provide a comprehensive understanding of a patient’s cardiovascular condition or the intricate risk factors influencing CVDs. Conventional ECG diagnostic techniques often overlook the incorporation of crucial clinical data, leading to limited diagnostic precision and missed insights.

Keywords: Cardiovascular disease (CVD), Electrocardiogram (ECG), Deep learning, Hybrid model, Convolutional Neural Network (CNN), Squeeze Net, Fully connected neural network (FCNN), Medical history integration, Disease classification, Health data fusion

Paper Id: 232631

Published On: 2025-07-11

Published In: Volume 13, Issue 4, July-August 2025

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