Multi-Stage Segmentation and Cascade Classification Methods for Improving Cardiac MRI Analysis
Journal:
arXiv
Published Date:
Dec 12, 2024
Abstract
The segmentation and classification of cardiac magnetic resonance imaging are
critical for diagnosing heart conditions, yet current approaches face
challenges in accuracy and generalizability. In this study, we aim to further
advance the segmentation and classification of cardiac magnetic resonance
images by introducing a novel deep learning-based approach. Using a multi-stage
process with U-Net and ResNet models for segmentation, followed by Gaussian
smoothing, the method improved segmentation accuracy, achieving a Dice
coefficient of 0.974 for the left ventricle and 0.947 for the right ventricle.
For classification, a cascade of deep learning classifiers was employed to
distinguish heart conditions, including hypertrophic cardiomyopathy, myocardial
infarction, and dilated cardiomyopathy, achieving an average accuracy of 97.2%.
The proposed approach outperformed existing models, enhancing segmentation
accuracy and classification precision. These advancements show promise for
clinical applications, though further validation and interpretation across
diverse imaging protocols is necessary.
