Researchers developed and internally validated an explainable machine learning model incorporating four viral and immune biomarkers to predict in-hospital mortality ...
A prospective study demonstrates that MVFANet combines color fundus photography and optical coherence tomography to classify myopia-related fundus pathologies across ...
A hybrid deep-learning model combining ConvNeXt and Swin Transformer architectures demonstrated 95.0 percent nine-class accuracy for diagnosing lumbar disc herniation ...
Researchers evaluating a multi-branch deep learning model on a 500-student physical education dataset found that the architecture failed to outperform ...
Computer scientists have developed an ensemble machine learning framework combining Boruta feature selection and XGBoost that achieves 98.78 percent accuracy ...
Evaluating pretrained transformer models on abusive Bangla social media posts revealed high in-corpus classification accuracy but highlighted substantial performance declines ...
In an evaluation of six deep learning models for breast cancer HER2-SISH classification, DeiT-Small achieved 96.28 percent test accuracy following ...
Researchers demonstrated that machine-learning classifiers analyzing radiomic features from noncontrast postmortem computed tomography can reliably detect autopsy-confirmed myocardial infarction.
