Machine Learning based Cardiac Magnetic Resonance Imaging (CMRI) for Cardiac Disease Detection

The electrocardiogram (ECG) is a graphical representation of the heart’s electrical activity generated by contraction and relaxation of the heart muscle. An ECG is a vital tool for diagnosing heart conditions. The ECG flag is required for patient care. Early detection of heart disease allows specialists to differentiate between heart illnesses. A growing number of heart diseases necessitated the development of automatic abnormality detection techniques to relieve physicians. Cardiac magnetic resonance (CMR) images are becoming increasingly important in the diagnosis and monitoring of cardiovascular diseases in the nanomaterial of the kernels. As a result of the large amount and diversity of the data available, there are still many unanswered questions when it comes to the description and characterization of nanomaterial. Biomaterials characterization requires minimal information, which can be provided by AI and machine learning algorithms. These representations are also intended to provide an estimate of the CMR image quality in order to facilitate better interpretation and analysis of the CMR images. Also investigated, how quantitative analysis can be used to benefit from the use of these learned image representations during the process of image synthesis.