92 275

Cited 1 times in

Analysis of facial ultrasonography images based on deep learning

Authors
 Kang-Woo Lee  ;  Hyung-Jin Lee  ;  Hyewon Hu  ;  Hee-Jin Kim 
Citation
 SCIENTIFIC REPORTS, Vol.12(1) : 16480, 2022-10 
Journal Title
SCIENTIFIC REPORTS
Issue Date
2022-10
MeSH
Deep Learning* ; Face* / diagnostic imaging ; Humans ; Ultrasonography* / methods
Abstract
Transfer learning using a pre-trained model with the ImageNet database is frequently used when obtaining large datasets in the medical imaging field is challenging. We tried to estimate the value of deep learning for facial US images by assessing the classification performance for facial US images through transfer learning using current representative deep learning models and analyzing the classification criteria. For this clinical study, we recruited 86 individuals from whom we acquired ultrasound images of nine facial regions. To classify these facial regions, 15 deep learning models were trained using augmented or non-augmented datasets and their performance was evaluated. The F-measure scores average of all models was about 93% regardless of augmentation in the dataset, and the best performing model was the classic model VGGs. The models regarded the contours of skin and bones, rather than muscles and blood vessels, as distinct features for distinguishing regions in the facial US images. The results of this study can be used as reference data for future deep learning research on facial US images and content development.
Files in This Item:
T202300447.pdf Download
DOI
10.1038/s41598-022-20969-z
Appears in Collections:
2. College of Dentistry (치과대학) > Dept. of Oral Biology (구강생물학교실) > 1. Journal Papers
Yonsei Authors
Kim, Hee Jin(김희진) ORCID logo https://orcid.org/0000-0002-1139-6261
Lee, Kang Woo(이강우)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/192997
사서에게 알리기
  feedback

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse

Links