Publications by authors named "GwiSeong Moon"
Very fast, high-resolution aggregation 3D detection CAM to quickly and accurately find facial fracture areas.
Comput Methods Programs Biomed
November 2024
Background And Objective: The incidence of facial fractures is on the rise globally, yet limited studies are addressing the diverse forms of facial fractures present in 3D images. In particular, due to the nature of the facial fracture, the direction in which the bone fractures vary, and there is no clear outline, it is difficult to determine the exact location of the fracture in 2D images. Thus, 3D image analysis is required to find the exact fracture area, but it needs heavy computational complexity and expensive pixel-wise labeling for supervised learning.
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- Wireless capsule endoscopy (WCE) allows for non-invasive detection of gastrointestinal diseases but struggles with obstacles like food debris, which impacts traditional machine learning models that rely heavily on color data.
- This study introduces a new model combining convolutional neural networks (CNN) and long short-term memory (LSTM) networks to analyze sequential image data, improving organ classification in challenging visual conditions.
- The developed model showed impressive results, achieving over 95% accuracy for stomach, small intestine, and colon classification, with additional performance metrics indicating its efficacy even with imbalanced datasets.
Facial bone fractures are relatively common, with the nasal bone the most frequently fractured facial bone. Computed tomography is the gold standard for diagnosing such fractures. Most nasal bone fractures can be treated using a closed reduction.
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