https://www.selleckchem.com/products/abbv-744.html
Analyzing and interpreting cone-beam computed tomography (CBCT) images is a complicated and often time-consuming process. In this study, we present two different architectures of multi-channel deep learning (DL) models "Ensemble" and "Synchronized multi-channel", to automatically identify and classify skeletal malocclusions from 3D CBCT craniofacial images. These multi-channel models combine three individual single-channel base models using a voting scheme and a two-step learning process, respectively, to simultaneously extract and lea
Like
Comment
Share
