Sepsis is a life-threatening systemic inflammatory response syndrome, often triggered by infection, which can rapidly progress to multiple organ failure and death. Clinical observations have shown that many patients develop sepsis in the presence of one or more chronic comorbidities, such as diabetes, chronic kidney disease, or cancer. These comorbidities may significantly influence both the…
Brain tumor detection and classification from MRI images are crucial tasks in medical imaging. This study focuses on utilizing semantic segmentation models and deep learning techniques to automate the segmentation and classification of various brain tumors, including Glioblastoma Multiforme (GBM), one of the most aggressive types. The research explores advanced segmentation models such as UNet,…
Alzheimer’s disease is a common neurodegenerative disorder and the leading cause of dementia in the elderly, characterized by memory loss, language difficulties, and cognitive decline. Its causes involve abnormal brain protein accumulation, genetics, and aging. While there is no cure, early detection can slow progression and improve quality of life. This study proposes a multi-omics…
Moyamoya Disease (MMD) is a rare and progressive cerebrovascular disorder characterized by the gradual narrowing or occlusion of the intracranial internal carotid arteries and their major branches. This process leads to the compensatory proliferation of collateral vessels, forming the characteristic “moyamoya” network. MMD can result in severe ischemic or hemorrhagic strokes, with a particularly high…
Epilepsy is a common chronic neurological disorder characterized by recurrent abnormal brain electrical activity. Due to the wide range of epilepsy types and varied clinical manifestations, diagnosis traditionally depends on the interpretation of EEG (electroencephalography) signals by experienced physicians—a process that is often time-consuming and subjective. In recent years, the need for objective and automated…
This research aims to establish a reliable classification framework for the influenza virus using genomic image transformation and deep learning. The system employs cellular automata to transform viral gene sequences into structured image formats, facilitating virus visualization, identification, and subtyping. This approach has the potential to generate a more accurate understanding of viral detection and…
This study proposes a hybrid deep learning framework that integrates object detection and classification models to achieve a fully automated assessment of intervertebral disc health. The system first employs the YOLOv7 model to accurately locate intervertebral discs within DICOM images, extracting regions of interest (ROIs) while preserving anatomical features and eliminating irrelevant tissue interference. To…
Project Overview This project aims to build an integrated system for competitive table tennis using intelligent technology and multidisciplinary research. Key areas include talent selection, pressure assessment, injury prevention, and tactical analysis. This aims to improve player performance, inform opponent strategies, and advance both table tennis and sports science. By bringing together experts from various…
Project Review The main purpose is to build a deep learning model for the detection of breast tumors in ultrasound images. This will help doctors to reduce the time it takes to read ultrasound images and to diagnose breast tumors more accurately, thereby helping patients to receive treatment as early as possible. This project is…
Project Overview As fingerprint recognition and retina scanning technology become increasingly widespread in the field of biometrics, scientists have begun to focus on brain science. Because the brain waves generated by each person are unique when stimulated by external stimuli, non-invasive electrodes can be attached to the cerebral cortex to record brain wave fluctuations using…
