Method of cardiac MRT of classification based on deep learning cascade models

Cardiac MRI is a key method for diagnosing cardiovascular diseases, offering detailed insights into heart structure and function. However, the complexity of cardiac pathologies requires advanced methods for accurate diagnosis. This paper proposes an improved method for classifying heart diseases using a cascade of binary classifiers based on deep learning. By leveraging this cascade approach, the system is able to break down the classification process into multiple stages, each focusing on a specific disease, which enhances the overall accuracy and reliability of the diagnosis. The proposed model is designed to accurately identify a range of heart diseases, including hypertrophic cardiomyopathy, myocardial infarction, dilated cardiomyopathy, and right ventricular abnormalities. The use of a cascade of classifiers enables a more efficient classification process by dividing the task into smaller, manageable subtasks. Each classifier in the cascade is specialized in detecting a particular pathology, allowing the model to focus on the unique characteristics of each disease. This approach reduces the risk of misclassification between similar conditions and improves the overall accuracy of the model. The method achieves an impressive average accuracy of 97.2%, which surpasses the results of known approaches. In particular, individual classifiers demonstrate up to 100% accuracy in detecting hypertrophic cardiomyopathy and right ventricular abnormalities, showcasing the precision of the model in these areas. For myocardial infarction and dilated cardiomyopathy, the method achieves an accuracy of 90%, which, although slightly lower, still represents a high level of diagnostic performance. These results highlight the significant potential of this method for clinical application, offering a more reliable tool for the diagnosis of complex heart conditions. However, the findings also emphasize the necessity of further development, particularly in cases involving less typical or more challenging pathologies. Future work will focus on refining the model and expanding its applicability across a broader range of clinical scenarios to ensure its robustness and effectiveness in real-world settings.