Museum collection images are invaluable for preserving cultural heritage and studying history. However, these images often lack quality and clarity. This study introduces a novel museum collection image enhancement technique based on fuzzy set theory...
This study aimed to validate the utility of commercially available vendor-neutral deep learning (DL) image enhancement software for improving the image quality of multiparametric MRI for gliomas in a multinational setting. A total of 294 patients fro...
BACKGROUND: Underwater environments face challenges with image degradation due to light absorption and scattering, resulting in blurring, reduced contrast, and color distortion. This significantly impacts underwater exploration and environmental moni...
Vascular contrast enhancement is crucial for early disease diagnosis and surgical precision in robotic surgery imaging. Traditional white-light imaging often fails to distinguish blood vessels due to the spectral similarity between vessels and surrou...
Deep learning-based fundus image enhancement has attracted extensive research attention recently, which has shown remarkable effectiveness in improving the visibility of low-quality images. However, these methods are often constrained to specific dat...
PURPOSE: To compare the image quality and diagnostic performance of periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER), reduced field-of-view (rFOV), and conventional diffusion-weighted imaging (cDWI) combined wi...
OBJECTIVE: This study aims to assess diagnostic performance of high-resolution dynamic contrast-enhanced (DCE) MRI with deep learning-based compressed sensing and super-resolution (DLCS-SR) reconstruction for identifying microadenomas.
In this study, we evaluate the performance of four deep learning models, EfficientNetB0, ResNet50, DenseNet121, and InceptionV3, for the classification of citrus diseases from images. Extensive experiments were conducted on a dataset of 759 images di...
PURPOSE: Three-dimensional time-of-flight magnetic resonance angiography (TOF-MRA) is effective for cerebrovascular disease assessment, but clinical application is limited by long scan times and low spatial resolution. Recent advances in deep learnin...
OBJECTIVES: To develop an automated deep learning (DL) methodology for detecting small hepatocellular carcinoma (sHCC) in cirrhotic livers, leveraging Gd-EOB-DTPA-enhanced MRI.
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