Ensembled Deep Learning Technique for Cardiovascular Disease Detection: A CNN-GRU Approach

Innovative early diagnosis and intervention methods are needed to combat cardiovascular disease (CVD), the primary cause of death globally. In recent years, Deep Learning (DL) algorithms have become strong medical image analysis and clinical decision support tools. This presentation covers DL advances and applications in cardiovascular disease detection. Deep learning algorithms especially CNNs did well in medical imaging modalities like computed tomography, angiography, and cardiac magnetic resonance imaging. These algorithms extract features well, allowing accurate heart structure segmentation, abnormality identification, and function evaluation. DL-based methods may also identify subtle anatomical and pathological changes, aiding early diagnosis and risk assessment. Data quality, interpretability, and regulatory compliance must be addressed when using DL models in clinical practice. To assure the dependability and ethics of DL-based cardiovascular care solutions, explainable Artificial Intelligence (AI), federated learning and regulatory frameworks must improve model robustness, transparency and validation.