Tongue Segmentation Using the Receptive Field Diversification of U-net (U-네트의 수용 영역 다양화를 이용한 혀 영역 분할)

LIYUJIE (이옥걸); Jung， Sung-Tae (정성태)

doi:10.9708/jksci.2021.26.09.037

Tongue Segmentation Using the Receptive Field Diversification of U-net

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2021, 26(9), pp.37-47
DOI : 10.9708/jksci.2021.26.09.037
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : August 24, 2021
Accepted : September 24, 2021
Published : September 30, 2021

LIYUJIE ¹, Jung， Sung-Tae ¹

¹원광대학교

Accredited

ABSTRACT

In this paper, we propose a new deep learning model for tongue segmentation with improved accuracy compared to the existing model by diversifying the receptive field in the U-net. Methods such as parallel convolution, dilated convolution, and constant channel increase were used to diversify the receptive field. For the proposed deep learning model, a tongue region segmentation experiment was performed on two test datasets. The training image and the test image are similar in TestSet1 and they are not in TestSet2. Experimental results show that segmentation performance improved as the receptive field was diversified. The mIoU value of the proposed method was 98.14% for TestSet1 and 91.90% for TestSet2 which was higher than the result of existing models such as U-net, DeepTongue, and TongueNet.

KEYWORDS

Tongue, Segmentation, Deep Learning, U-net, Receptive Field

Citation status

* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Tongue Segmentation Using the Receptive Field Diversification of U-net

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Tongue Segmentation Using the Receptive Field Diversification of U-net

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.