Deep Learning Framework for Automatic Cardiac Diagnosis

Abstract

Cardiovascular diseases (CVDs) continue a leading basis of death globally, underscoring the critical necessity for efficient and accurate investigative tools. Deep learning, a subset of artificial intelligence, has arose as an encouraging approach for automatic cardiac diagnosis due to its capability to extract complex features from medical imaging data. In this study, we propose a novel deep learning framework tailored for the automatic diagnosis of cardiac conditions from medical images, such as echocardiograms, MRI scans, or CT scans. The framework employs convolutional neural networks (CNNs) for feature mining and classification tasks. It comprises several key components, including data preprocessing, feature extraction using pre-trained CNN architectures (such as VGG, ResNet, or DenseNet), fine-tuning, and classification using fully connected layers. To address the challenges of limited annotated medical imaging data, transfer learning techniques are incorporated to adapt the pre-trained models to the specific cardiac diagnosis task. Furthermore, to enhance model generalization and interpretability, attention mechanisms and explainable AI techniques are incorporated into the framework. Attention methodologies enable the model to emphasis on relevant regions within the medical images, aiding in more accurate diagnosis. Explainable AI techniques provide insights into the decision-making process of the deep learning model, increasing trust and transparency in its predictions. The proposed framework is evaluated on a diverse dataset comprising cardiac imaging data from multiple modalities and cardiac conditions. Performance metrics such as sensitivity, accuracy, area under the receiver operating characteristic curve (AUC-ROC), and specificity are used to measure the diagnostic accuracy of the model. Experimental results validate the effectiveness of the proposed framework in accurately diagnosing various cardiac conditions, including myocardial infarction, cardiomyopathy, and valvular heart diseases. In conclusion, the developed deep learning framework shows promising potential as an automated tool for cardiac diagnosis, offering rapid and accurate assessment of cardiac conditions from medical imaging data. By leveraging the supremacy of deep learning and incorporating attention mechanisms and explainable AI techniques, the framework aims to improve clinical decision-making and patient results in the field of cardiology.