A5103173471- Brain Tumor Detection and Classification
- Dementia and Cognitive Impairment Research
- Functional Brain Connectivity Studies
- Digital Games and Media
- Radiomics and Machine Learning in Medical Imaging
- Human Motion and Animation
- Digital Radiography and Breast Imaging
- Advanced Data Processing Techniques
- Computational Physics and Python Applications
- Alzheimer's disease research and treatments
- Educational Games and Gamification
- Neonatal and fetal brain pathology
- Medical Imaging and Analysis
Konyang University
2022
Abstract The classification of Alzheimer’s disease (AD) using deep learning methods has shown promising results, but successful application in clinical settings requires a combination high accuracy, short processing time, and generalizability to various populations. In this study, we developed convolutional neural network (CNN)-based AD algorithm magnetic resonance imaging (MRI) scans from patients age/gender-matched cognitively normal controls two populations that differ ethnicity education...
Objective A deep learning-based classification system (DLCS) which uses structural brain magnetic resonance imaging (MRI) to diagnose Alzheimer’s disease (AD) was developed in a previous recent study. Here, we evaluate its performance by conducting single-center, case-control clinical trial.Methods We retrospectively collected T1-weighted MRI scans of subjects who had an accompanying measure amyloid-beta (Aβ) positivity based on 18F-florbetaben positron emission tomography scan. The dataset...
Abstract Objective To investigate diagnostic performance of a deep learning-based classification system using structural brain MRI (DLCS) for Alzheimer’s disease (AD). Methods A single-center, case-control clinical trial was conducted. T1-weighted scans 188 patients with mild cognitive impairment or dementia due to AD and 162 cognitively normal controls were retrospectively collected. The amyloid beta (Aβ)-positive, whereas the Aβ-negative, on 18F-florbetaben positron emission tomography....
Chest radiography is the most common method of examining chest disease. However, interpretation X-rays difficult, and diagnosis may vary depending on doctor's proficiency. In order to solve this problem, additional using a computer attracting attention in medical imaging field. addition, recently developed artificial intelligence technology has been applied analysis X-rays, commercialization entered stage as computer-aided diagnostic tool. reading model based different performance type data....