Automatic Classification of Wound Images Showing Healing Complications: Towards an Optimised Approach for Detecting Maceration
Dührkoop E, Malihi L, Erfurt-Berge C, Heidemann G, Przysucha M, Busch D, Hübner UH (2024)
Publication Type: Conference contribution
Publication year: 2024
Journal
Publisher: IOS Press BV
Series: Studies in Health Technology and Informatics
City/Town: Amsterdam
Book Volume: 317
Pages Range: 347-355
Conference Proceedings Title: German Medical Data Sciences 2024. Health – Thinking, Researching and Acting Together. Proceedings of the 69th Annual Meeting of the German Association of Medical Informatics, Biometry, and Epidemiology e.V. (gmds) 2024 in Dresden, Germany
Event location: Dresden
ISBN: 9781643685366
DOI: 10.3233/SHTI240877
Abstract
This study aims to advance the field of digital wound care by developing and evaluating convolutional neural network (CNN) architectures for the automatic classification of maceration, a significant wound healing complication, in 458 annotated wound images. Detection and classification of maceration can improve patient outcomes. Several CNN models were compared and MobileNetV2 emerged as the top-performing model, achieving the highest accuracy despite having fewer parameters. This finding underscores the importance of considering model complexity relative to dataset size. The study also explored the role of image cropping and the use of Grad-CAM visualizations to understand the decision-making process of the CNN. From a medical perspective, results indicate that employing CNNs for classification of maceration may enhance diagnostic accuracy and reduce the clinicians' time and effort.
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How to cite
APA:
Dührkoop, E., Malihi, L., Erfurt-Berge, C., Heidemann, G., Przysucha, M., Busch, D., & Hübner, U.H. (2024). Automatic Classification of Wound Images Showing Healing Complications: Towards an Optimised Approach for Detecting Maceration. In Rainer Rohrig, Niels Grabe, Ursula Hertha Hubner, Klaus Jung, Ulrich Sax, Carsten Oliver Schmidt, Martin Sedlmayr, Antonia Zapf (Eds.), German Medical Data Sciences 2024. Health – Thinking, Researching and Acting Together. Proceedings of the 69th Annual Meeting of the German Association of Medical Informatics, Biometry, and Epidemiology e.V. (gmds) 2024 in Dresden, Germany (pp. 347-355). Dresden, DE: Amsterdam: IOS Press BV.
MLA:
Dührkoop, Eric, et al. "Automatic Classification of Wound Images Showing Healing Complications: Towards an Optimised Approach for Detecting Maceration." Proceedings of the 69th Annual Meeting of the German Association of Medical Informatics, Biometry and Epidemiology, GMDS 2024, Dresden Ed. Rainer Rohrig, Niels Grabe, Ursula Hertha Hubner, Klaus Jung, Ulrich Sax, Carsten Oliver Schmidt, Martin Sedlmayr, Antonia Zapf, Amsterdam: IOS Press BV, 2024. 347-355.
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