Hyperspectral imaging has shown significant applicability in the medical field, particularly for its ability to represent spectral information that can differentiate specific biomolecular characteristics in tissue samples. However, the complexity of ...
Pulmonary embolism (PE) represents a severe, life-threatening cardiovascular condition and is notably the third leading cause of cardiovascular mortality, after myocardial infarction and stroke. This pathology occurs when blood clots obstruct the pul...
Human Activity Recognition (HAR) has become an active research area in recent years due to its applicability in various domains and the growing need for convenient facilities and intelligent homes for the elderly. Physical activity tends to decrease ...
Osteoarthritis (OA) is a prevalent condition among athletes, characterized by the progressive degradation of joint cartilage, particularly in weight-bearing joints such as the knees. Early detection is critical for effective management and prevention...
Myopia is a growing global health concern, and early detection and intervention are crucial for preventing its onset and progression. This study aims to predict myopia onset one year in advance by integrating biometric measurements with multimodal im...
Brain tumor classification (BTC) from Magnetic Resonance Imaging (MRI) is a critical diagnosis task, which is highly important for treatment planning. In this study, we propose a hybrid deep learning (DL) model that integrates VGG16, an attention mec...
The expansion of the Internet of Medical Things (IoHT) presents significant advantages for healthcare over improved data-driven insights and connectivity and offers critical cybersecurity challenges. Attacks are a serious risk for neural network secu...
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by genetic predisposition and alterations in brain structural connectivity. While existing studies have established associations between genetic variants a...
OBJECTIVE: This study aims to develop a robust and clinically applicable framework for preoperative grading of meningiomas using T1-contrast-enhanced and T2-weighted MRI images. The approach integrates radiomic feature extraction, attention-guided de...
We present a novel computational framework that combines Agent-Based Modeling (ABM) with Reinforcement Learning (RL) using the Double Deep Q-Network (DDQN) algorithm to determine cellular behavior in response to environmental signals. With this appro...
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