• Farheen Siddiqui
• Dr. Shobhit Sinha
• Dr. Yusuf Perwej
• Sarvesh Kumar
• Dr. Nikhat Akhtar

Cardiovascular disorders are among the primary causes of mortality and morbidity globally. Early detection requires consistent monitoring of several clinical and lifestyle variables. This explains the increasing number of researches aimed at automating the prediction of cardiac disorders, starting with the analysis of ECG pictures, which is the first diagnostic test administered to patients and is also the most straightforward and cost-effective to execute. Current study presents various heart disease diagnostic systems using diverse advanced methodologies; however, enhancing these procedures remains a compelling research domain. This study proposes a smart healthcare system for diagnosing heart illness via ECG data. This paper presents a deep learning system for detecting cardiovascular problems using electrocardiogram (ECG) pictures. The Convolutional Neural Network (CNN) was trained and assessed, reaching a classification accuracy of 99.1%. The suggested approach illustrates the capability of deep learning models in facilitating cardiovascular diagnostics and underscores the need of accessible deployment tools. Subsequent study will concentrate on more extensive datasets, supplementary heart diseases, and model interpretability using explainable AI methodologies.

Smart Systems, Convolutional Neural Networks (CNN), Deep Learning, ECG Signals, Heart Disease, Medical Images.

"Deep Learning-Based Analysis of ECG Images for Intelligent Cardiovascular Diagnosis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 6, page no.j252-j259, June-2025, Available :http://www.jetir.org/papers/JETIR2506930.pdf

"Deep Learning-Based Analysis of ECG Images for Intelligent Cardiovascular Diagnosis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 6, page no. ppj252-j259, June-2025, Available at : http://www.jetir.org/papers/JETIR2506930.pdf