Automated tongue segmentation using deep encoder-decoder model
3
Issued Date
2023-01-01
Resource Type
ISSN
13807501
eISSN
15737721
Scopus ID
2-s2.0-85150375955
Journal Title
Multimedia Tools and Applications
Rights Holder(s)
SCOPUS
Bibliographic Citation
Multimedia Tools and Applications (2023)
Suggested Citation
Kusakunniran W., Borwarnginn P., Imaromkul T., Aukkapinyo K., Thongkanchorn K., Wattanadhirach D., Mongkolluksamee S., Thammasudjarit R., Ritthipravat P., Tuakta P., Benjapornlert P. Automated tongue segmentation using deep encoder-decoder model. Multimedia Tools and Applications (2023). doi:10.1007/s11042-023-15061-1 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/81794
Title
Automated tongue segmentation using deep encoder-decoder model
Other Contributor(s)
Abstract
This paper proposes a solution of tongue segmentation in images. The solution relies on a convolutional neural network, using deep U-Net with deep layers of encoder-decoder modules. The model is trained with a starting resolution of 512 x 512 pixels. To enhance the segmentation performances of the trained model across recording environments, three main types of data augmentations are added in the training process, including additive gaussian noise, multiply and add to brightness, and change color temperature. They could also handle an inadequate number of data samples in the limited datasets. The proposed method is evaluated based on four measurement metrics of Dice coefficient, mean IoU, Jaccard distance, and accuracy. The model is successfully trained on publicly available datasets, and then transferred to be tested with the self-collected dataset in the real-world environment.