Heart Disease Classification from Echocardiogram Images Using Deep Learning

Heart disease is a leading cause of morbidity and mortality worldwide, demanding efficient and accurate diagnostic methods. Echocardiography is a widely used imaging modality for assessing cardiac health; however, manual interpretation of echocardiogram images requires extensive expertise and remains prone to subjectivity. This research proposes a deep learning framework for automated heart disease classification using echocardiogram images. A modified VGG16-inspired convolutional neural network (CNN) architecture was developed, integrating batch normalization, dropout regularization, and global max pooling to enhance generalization and reduce overfitting. The model was trained and validated on a curated echocardiogram dataset, utilizing data augmentation to improve robustness. Evaluation was performed using metrics such as accuracy, precision, recall, F1-score, specificity, and confusion matrix analysis. Experimental results demonstrated that the proposed model achieved superior performance, with accuracy exceeding 92% and strong classification consistency across multiple classes. Compared to standard deep learning baselines, the architecture showed significant improvement in both sensitivity and specificity, highlighting its potential for clinical deployment. This study concludes that deep learning–driven echocardiogram analysis can serve as an effective decision support tool, aiding clinicians in early detection and classification of cardiovascular diseases, thereby contributing to improved diagnosis and patient outcomes.

Index Terms— Deep learning, echocardiography, heart disease classification, convolutional neural networks (CNNs).