Wound image segmentation using deep convolutional neural network

Hyunyoung Kang; Kyungdeok Seo; Sena Lee; Byung Ho Oh; Sejung Yang

doi:10.1117/12.2649913

14 March 2023 Wound image segmentation using deep convolutional neural network

Hyunyoung Kang, Kyungdeok Seo, Sena Lee, Byung Ho Oh, Sejung Yang

Proceedings Volume 12352, Photonics in Dermatology and Plastic Surgery 2023; 123520F (2023) https://doi.org/10.1117/12.2649913
Event: SPIE BiOS, 2023, San Francisco, California, United States

Conference Poster

Abstract

Traditional methods of wound diagnosis have been diagnosed and prescribed by the naked eye of an expert. If the wound segmentation algorithm is applied to the wound diagnosis, the area of wound can be quantitated and used as an auxiliary means of treatment. Even with dramatic development of Deep learning technology in recent years, However, a lack of datasets generally occurs overfitting problem of deep learning model, which leads to poor performance for external datasets. Therefore, we trained the wound segmentation model by adding a new wound dataset in addition to the existing Open dataset, the Diabetic Foot Ulcer Challenge Dataset. Machine learning based methods are used when producing new dataset, ground truth images. Thus, in addition to the manual methods, Gradient Vector Flow machine learning techniques is used for ground-truth image production to reduce the time consumed in vain. The wound segmentation model used in this study is a U-net with residual block combined with cross entropy loss and Dice loss. As a result of the experiment, the wound segmentation accuracy was about 90% for Dice coefficient

Citation Download Citation

Hyunyoung Kang, Kyungdeok Seo, Sena Lee, Byung Ho Oh, and Sejung Yang "Wound image segmentation using deep convolutional neural network", Proc. SPIE 12352, Photonics in Dermatology and Plastic Surgery 2023, 123520F (14 March 2023); https://doi.org/10.1117/12.2649913

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available