Echocardiographic Image Analysis for Heart Disease Detection Via Deep Neural Networks

Heart disease remains one of the leading causes of death worldwide, making early and accurate diagnosis extremely important. Echocardiography is a widely used medical imaging technique that provides real-time visualization of the heart’s structure and function. However, manual interpretation of echocardiogram images requires expert cardiologists and can sometimes lead to inconsistent results. This research proposes a deep neural network-based system for the automatic detection of heart diseases from echocardiographic images. The proposed model uses convolutional neural networks to extract important features from ultrasound images and classify them into normal or abnormal heart conditions. The system performs image preprocessing, feature extraction, model training, and classification to achieve high accuracy in disease detection. Experimental results show that the deep learning model significantly improves diagnostic efficiency and reliability. This approach can assist healthcare professionals in faster and more accurate heart disease diagnosis.