Design of an improved method for cardiac MRI lesion analysis using CMFT-GAN and spatio-temporal clustering process.

Early and precise diagnosis is difficult due to the limitations of cardiac MRI lesion analysis, which include low spatial resolution, undersampled datasets, and insufficient lesion diversity. Because of poor feature regularization, insufficient spatiotemporal dynamics modeling, and a lack of pathology-aware augmentation, current systems frequently generate high false positives. We present a thorough enhancement and clustering framework that reinterprets lesion analysis in cardiac MRI in order to get past these obstacles. Our method combines motion-aware lesion consistency, synthesizes various pathological variations, and integrates multi-domain anatomical knowledge. Additionally, it uses a curriculum-based, progressive learning approach for classification and segmentation and guarantees structural alignment in feature space. Performance is greatly improved by this integrated approach: PSNR rises from 25.6 to 31.4 dB, SSIM rises from 0.74 to 0.89, the boundary F1-score rises from 0.67 to 0.81, and the dice coefficient rises from 0.72 to 0.88. The overall classification accuracy of 93.2% establishes a new standard for the evaluation of cardiac MRI lesions.•Transferring anatomical knowledge from well-annotated domains to improve cardiac MRI across multiple modalities.•To increase lesion diversity and classification robustness, use pathology-aware clustering and data augmentation.•A curriculum-driven hierarchical learning pipeline that incorporates structural, temporal, and spatial consistency.