High-resolution late gadolinium enhancement imaging is a powerful tool for arrhythmia risk assessment post-myocardial infarction, but requires substantial operator time and expertise to analyze. To address this challenge, automated analysis is introduced to isolate and depict relevant image features corresponding to healthy myocardium, peri-infarct gray zone, and dense scar. Using two sets of manual epicardial and endocardial contours, weighted total variation denoising is used to correct for statistical noise, and persistent homology is used to stratify topological features of the image. K-means clustering was used to generate remote myocardium and dense scar signal intensities for automated FWHM thresholding.
The field of astronomy has made tremendous progress in recent years thanks to advancements in technology and the development of sophisticated algorithms. One area of interest for astronomers is the classification of galaxy morphology, which involves categorizing galaxies based on their visual appearance. However, with the sheer number of galaxy images available, it would be a daunting task to manually classify them all. To address this challenge, a novel Residual Neural Network (ResNet) model, called ResNet_Var, that can classify galaxy images is proposed in this study. Subsets of the Galaxy Zoo 2 dataset are used in this research, one contains over 28,000 images for the five-class classification task, and the other contains over 25,000 images for the seven-class classification task. The overall classification accuracy of the ResNet_Var model was 95.35% for the five-class classification task and 93.54% for the sevenclass classification task.
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