A Deep Learning Model for Wound Size Measurement Using Fingernails
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Wound size is an important parameter in the evaluation of healing status of chronic wounds. Many technologies, such as software embedded digital camera or artificial intelligence assisted smart phone applications, have been applied to the automatic wound size measurement. However, these methods or devices are either expensive or inconvenient. Instead of using a ruler to measure wound size, we propose a novel method using fingernails as the reference objects with the combination of two deep learning models. The width of the nail was first detected and computed by RCNN deep learning (DL) model. After that, the width and height of the wound were inferred by those of the bounding box generated from YoloV5 DL model. The wound size can be obtained from the known nail width. The experimental results showed that the mean Pearson correlation coefficient reached 0.914 in comparing the prediction and the standard wound sizes. We believe our proposed model is a simple and effective method for wound size measurement.
Originalsprog | Engelsk |
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Titel | ICMHI' 2022 : Proceedings of the 6th International Conference on Medical and Health Informatics |
Antal sider | 6 |
Forlag | Association for Computing Machinery |
Publikationsdato | 2022 |
Sider | 141-146 |
ISBN (Elektronisk) | 978-1-4503-9630-1 |
DOI | |
Status | Udgivet - 2022 |
Begivenhed | 6th International Conference on Medical and Health Informatics, ICMHI 2022 - Virtual, Online, Japan Varighed: 12 maj 2022 → 15 maj 2022 |
Konference
Konference | 6th International Conference on Medical and Health Informatics, ICMHI 2022 |
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Land | Japan |
By | Virtual, Online |
Periode | 12/05/2022 → 15/05/2022 |
Bibliografisk note
Funding Information:
This work was supported by Ministry of Science and Technology Program. Grant no: MOST 110WHK1210054.
Publisher Copyright:
© 2022 ACM.
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