Abstract:
Cardiovascular diseases (CVDs) pose a significant threat to human health worldwide due to their severity and high prevalence. They are responsible for a substantial number of deaths each year. One widely used method for detecting cardiovascular abnormalities is the analysis of electrocardiogram (ECG) images, which provide valuable insights into the electrical activity of the heart. In this study, we employed a deep learning approach utilizing a convolutional neural network (CNN) to automate the detection of cardiovascular diseases using ECG images. The proposed model was trained using a diverse dataset consisting of ECG images from patients with different cardiac conditions. The training process involved leveraging the power of various Python libraries, including Keras and TensorFlow, to facilitate model development and optimization. The results obtained from our deep learning model are highly promising. The model achieved an impressive training accuracy of 99%, indicating its ability to effectively learn and classify ECG images during the training phase. Furthermore, the validation accuracy of 91% suggests that the model can generalize well to unseen data, which is crucial for real-world applications. Finally, the model demonstrated a test accuracy of 91%, confirming its robustness and potential for accurate cardiovascular disease detection.
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