Deep Learning Technology Based Cardiovascular Diagnosis Using ECG Images

This paper proposes a deep learning approach for early identification of cardiovascular diseases using Electrocardiogram (ECG) images. Utilizing a convolutional neural network (CNN), the project aims to detect the patterns indicative of various cardiac conditions. The training dataset comprises of well curated and processed ECG images covering a spectrum of cardiovascular studies. Through fine processing on this dataset, the model adapts its learned features to the unique characteristics inherent in ECG images. The network's capacity to capture hierarchical characteristics aids in identifying subtle anomalies indicative of underlying cardiovascular problems. We introduce a web application incorporating a CNN model in its backend for analyzing uploaded ECG images, providing predictions to users through a user interface. The system enables users to upload ECG images, which are then processed by the backend server, passed through the CNN model for prediction, and displayed back to the user via the frontend interface. This integration facilitates efficient and accessible ECG analysis, contributing to potential advancements in healthcare technology. This approach underscores the importance of timely diagnosis for effective patient care.