Breast cancer remains one of the most prevalent and life-threatening diseases among women worldwide, necessitating early and accurate detection methods. Traditional diagnostic approaches often face limitations in sensitivity and specificity, highligh...
Recent research advances have resulted in an experimental benchtop X-ray fluorescence computed tomography (XFCT) system that likely meets the imaging dose/scan time constraints for benchtop XFCT imaging of live mice injected with gold nanoparticles (...
Timely detection of abnormal cardiotocography (CTG) during labor plays a crucial role in enhancing fetal prognosis. Recent research has explored the use of deep learning for CTG interpretation, most studies rely on small, localized datasets or focus ...
BACKGROUND: The growing availability of electronic health records (EHRs) presents an opportunity to enhance patient care by uncovering hidden health risks and improving informed decisions through advanced deep learning methods. However, modeling EHR ...
This study focuses on the impact of learning experience on college students' deep learning of English and the chain-mediated effects of motivation and strategy. In the context of globalization, English is crucial for university students, but traditio...
With the development of smart wearable devices and deep learning (DL) technology, the monitoring and analysis of daily sports activities of teenagers face new opportunities. At present, traditional CNN (Convolutional Neural Network) models are mostly...
Malaria continues to be a severe health problem across the globe, especially within resource-limited areas which lack both skilled diagnostic personnel and diagnostic equipment. This study investigates the use of deep learning diagnosis for malaria t...
As industrialization and the development of smart cities progress, effective waste collection, classification, and management have become increasingly vital. Recycling processes depend on accurately identifying and restoring waste materials to their ...
PURPOSE: To develop a deep learning model using orbital computed tomography (CT) imaging to accurately distinguish thyroid eye disease (TED) and orbital myositis, two conditions with overlapping clinical presentations.
OBJECTIVE: To investigate the effect of contrast enhancement on the diagnosis of interstitial lung abnormalities (ILA) in automatic quantitative CT measurement in patients with paired pre- and post-contrast scans.
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