Stress is an intensified reaction that occurs when humans experience challenges(stressors) due to complex and nonlinear responses. The study proposes a pattern-driven framework that combines signal and image-based modalities, incorporating Local Bina...
Cervical cancer ranks fourth globally in terms of both incidence and mortality among women, making timely diagnosis essential for effective treatment. Although the acetowhite regions and their margins are important for cervical cancer staging, their ...
OBJECTIVES: During emergency surgery, patients with acute type A aortic dissection (ATAAD) experience unfavourable outcomes throughout their hospital stay. The combination of total aortic arch replacement (TAR) and frozen elephant trunk (FET) implant...
Early detection of drought stress is critical for taking timely measures for reducing crop loss before the drought impact becomes irreversible. The subtle phenotypical and physiological changes in response to drought stress are captured by non-invasi...
BACKGROUND: This study aimed to develop and validate a multi-temporal magnetic resonance imaging (MRI)-based delta-radiomics model to accurately predict severe acute radiation enteritis risk in patients undergoing total neoadjuvant therapy (TNT) for ...
Globally, nervous system diseases are the leading cause of disability-adjusted life-years and the second leading cause of mortality in the world. Traditional diagnostic methods for nervous system diseases are expensive. So this study aimed to constru...
Accurate pain assessment is essential for effective management; however, most studies have focused on differentiating pain from non-pain or estimating pain intensity rather than distinguishing between distinct pain types. We present a machine learnin...
Hemodialysis stands as the most prevalent renal replacement therapy globally. Accurately identifying mortality among hemodialysis patients is paramount importance, as it enables the formulation of tailored interventions and facilitates timely managem...
The timely detection of abnormal health conditions is crucial in achieving successful medical intervention and enhancing patient outcomes. Despite advances in health monitoring, existing methods often struggle with achieving high accuracy, sensitivit...
To identify syrup adulteration in honey, a deep learning model based on the CNN-CBAM-SVM architecture combined with H NMR spectra was developed. The traditional CNN model was enhanced by incorporating the CBAM module and replacing the fully connected...
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