Medical image segmentation plays a critical role in modern clinical diagnosis. However, existing methods face challenges such as insufficient feature extraction, limited spatial modeling capabilities, and restricted generalization ability with low co...
Central Apnea (CA) and Central Hypopnea (CH) are sleep disorders arising from the brain's inability to signal respiratory muscles, potentially leading to severe complications such as heart failure. This study presents a novel system for automating CA...
Brain tumors pose a severe health risk, often leading to fatal outcomes if not detected early. While most studies focus on improving classification accuracy, this research emphasizes prediction certainty, quantified through loss values. Traditional m...
Parkinson's disease (PD) is a neurodegenerative disorder that occurs as a result of a decrease in the chemical called dopamine in the brain. There is no definitive treatment for PD, but some medications used to control symptoms in the early stages ha...
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by a progressive cognitive decline, necessitating early diagnosis for effective treatment. This study presents the Relational Bi-level Aggregation Graph Convolutional Network with...
This review paper comprehensively examines recent advancements in machine learning (ML) applications within biofluid mechanics, with a targeted focus on enabling clinically actionable diagnostics and simulations. It demonstrates how ML, and in partic...
The aim of this research is to help health care professionals to automatically detect lower urinary tract disorders using sounds of voiding recorded at home. In total 93 patients were diagnosed as obstructed or non-obstructed in a hospital using trad...
BACKGROUND: Prostate Cancer (PCa) is a severe disease that affects males globally. The Gleason grading system is a widely recognized method for diagnosing the aggressiveness of PCa using histopathological images. This system evaluates prostate tissue...
This study undertakes a comprehensive investigation into the impact of socioeconomic factors on the ecological footprint (EFP) in Turkey. It employs robust econometric methods and advanced Machine Learning (ML) models. The study employs Generalized L...
Predicting dengue distribution based on environmental factors is crucial for effective vector control and management as environmental factors like temperature, demographics, and artificial changes such as roads and buildings significantly influence d...
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