Heart Disease Classification From Echocardiogram Images Using Deep Learning

Heart disease remains one of the leading causes of death globally. Echocardiography is a commonly used method to diagnose cardiovascular conditions. However, accurately interpreting echocardiogram images requires specialized cardiovascular knowledge, making it difficult for those who are not expert in this field. This study aimed to address this challenge by developing advanced deep learning models capable of automatically classifying heart disease based on echocardiogram data. A deep learning model, inspired by VGG16, is specifically designed for this task. Following the development phase, the model underwent rigorous evaluation using various metrics such as classification measures, confusion matrices, and confidence tests to assess their performance. Upon analyzing the experimental results, it is observed that the proposed model demonstrated superior performance compared to existing deep learning models, achieving the highest scores in terms of both F1 score and accuracy. The experimental results suggests that the proposed method can be used in classification of cardiovascular diseases, potentially improving the early detection and prognostic.