Deep learning has achieved significant advancements in medical image segmentation, but existing models still face challenges in accurately segmenting lesion regions. The main reason is that some ...

Passive sensing via wearable devices and smartphones, combined with machine learning (ML), enables objective, continuous, and noninvasive mental health monitoring. Objective: This study aimed to ...

Abstract: Medical images are the standard approach for the analysis and diagnosis of critical issues of diseases. To minimize the time-consuming inspection and evaluation process of the medical images ...

Abstract: The central nervous system (CNS) contains the brain, spinal cord, and it governs all essential functions. These functions encompass cognition, verbal communication, and locomotion. When a ...

Abstract: The current variants of the Segment Anything Model (SAM), which include the original SAM and Medical SAM, still lack the capability to produce sufficiently accurate segmentation for medical ...

Background: Hip fractures are a major health concern in the older adults, severely impacting patients’ quality of life and straining healthcare systems. With China’s aging population, their incidence ...

Abstract: Human trafficking is a serious problem that requires new ideas and data-driven approaches to identify and solve. In this study, we analyze large datasets to identify patterns and anomalies ...

Abstract: Automated segmentation of the optic disc (OD) and the optic cup (OC) in retinal fundus images plays a pivotal role in early glaucoma diagnosis. Many studies have employed deep learning ...

Abstract: One of the prominent challenges encountered in real-world data is an imbalance, characterized by unequal distribution of observations across different target classes, which complicates ...

Abstract: This study focuses on the development of a stacked model, named Cluster Boost, which integrates K-means clustering and Gradient Boosting to analyse customer behaviour in e-commerce. Cluster ...