Unet based Image Segmentation of Sweat Droplets for Assisting Assessment of Atopic Dermatitis

被引：0

作者：

Miyaji, Yuma ^{[1
]}

Michida, Ryuichi ^{[1
]}

Koide, Tetsushi ^{[1
]}

Hayashida, Yuki ^{[2
]}

Aoyama, Yumi ^{[2
]}

机构：

[1] Hiroshima Univ, Res Inst Nanodevices, Higashihiroshima, Japan

[2] Kawasaki Med Sch, Dept Dermatol, Kurashiki, Okayama, Japan

来源：

2022 37TH INTERNATIONAL TECHNICAL CONFERENCE ON CIRCUITS/SYSTEMS, COMPUTERS AND COMMUNICATIONS (ITC-CSCC 2022) | 2022年

关键词：

Skin Surface Structure; Sweat Droplets; Im-pression Mold Technique (IMT); Deep Learning; U-Net; Image Segmentation;

D O I：

10.1109/ITC-CSCC55581.2022.9894957

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, we describe an automatic analysis that identifies skin structure and sweat droplets from skin surface images. The analysis uses a discriminator based on deep learning and has succeeded in producing results that in some cases exceed the diagnosis of a medical experts. The discriminator is built based on U-Net, which is used for image segmentation.

引用

页码：124 / 127

页数：4

共 9 条

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