In this study, we present a novel hybrid model combining the Vision Transformer (ViT) and Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) for thyroid nodule detection in ultrasound images. While traditional methods, such a...
Crime is a critical social issue that threatens public safety, and accurately predicting and reducing crime in hotspot areas is a key objective. Crime patterns are inherently complex, with substantial spatial and temporal variability, which makes acc...
Correct histopathological image classification of lung and colon cancer is a stringent challenge for clinical pathology. This work introduces a hybrid deep learning network by combining traditional handcrafted features of LBP, GLCM, wavelet, color, a...
Early-phase severe complications remain a major cause of morbidity and mortality during induction chemotherapy for acute leukaemia. Existing risk scores capture only limited prognostic variance and are rarely well-calibrated for clinical decision sup...
Accurate detection of user intention is a critical requirement for intelligent control systems in upper-limb rehabilitation robots. However, electromyography (EMG)-based recognition can degrade significantly under muscle fatigue. To address this limi...
Perioperative stroke significantly impacts postoperative outcomes. Current risk stratification methods for perioperative stroke prediction lack accuracy and practicality. We aimed to develop a machine learning (ML) model that improves both accuracy a...
Traditional taekwondo training methods face limitations in providing objective, real-time feedback for technique improvement, relying primarily on subjective instructor observations that may lack precision and consistency. This research presents an i...
Neural network models for outcome prediction play a pivotal role in neurological disease research, particularly for baseline risk assessment. Schizophrenia, a complex and relatively rare neuropsychiatric disorder, presents significant diagnostic chal...
Deep learning tools based on computer vision have emerged as alternative methods for assessing radiographic image patterns. These approaches have been explored for various forensic applications, including sex and age estimation. This study aimed to e...
Bladder tumours (BTs) pose significant clinical challenges due to their high recurrence rates and risk of progression to invasive malignancies, which emphasises the need for early and accurate detection. Magnetic resonance imaging (MRI), with its sup...
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