Publications by authors named "Jonghun Jeong"
Article Synopsis
- This study explores how a deep learning technique to reduce CT scan slice thickness from 5 mm to 1 mm can improve the detection of lung nodules by computer-aided detection (CAD) systems.
- The retrospective analysis of 687 chest CT scans showed a significant increase in CAD performance, with the area under the receiver operating characteristic curve rising from 0.867 to 0.902 and nodule-level sensitivity improving from 0.826 to 0.916.
- The findings suggest that using 1 mm CT scans not only enhances detection rates, particularly for smaller nodules, but also provides better clarity in identifying nodules compared to the 5 mm scans, making it beneficial for clinical practice.
High accuracy has been reported in deep learning classification for amyloid brain scans, an important factor in Alzheimer's disease diagnosis. However, the possibility of overfitting should be considered, as this model is fitted with sample data. Therefore, we created and evaluated an [18F]Florbetaben amyloid brain positron emission tomography (PET) scan classification model with a Dong-A University Hospital (DAUH) dataset based on a convolutional neural network (CNN), and performed external validation with the Alzheimer's Disease Neuroimaging Initiative dataset.
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