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An Image Based Object Recognition System for Wound Detection and Classification of Diabetic Foot and Venous Leg Ulcers

  • Venous leg ulcers and diabetic foot ulcers are the most common chronic wounds. Their prevalence has been increasing significantly over the last years, consuming scarce care resources. This study aimed to explore the performance of detection and classification algorithms for these types of wounds in images. To this end, algorithms of the YoloV5 family of pre-trained models were applied to 885 images containing at least one of the two wound types. The YoloV5m6 model provided the highest precision (0.942) and a high recall value (0.837). Its mAP_0.5:0.95 was 0.642. While the latter value is comparable to the ones reported in the literature, precision and recall were considerably higher. In conclusion, our results on good wound detection and classification may reveal a path towards (semi-) automated entry of wound information in patient records. To strengthen the trust of clinicians, we are currently incorporating a dashboard where clinicians can check the validity of the predictions against their expertise.

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Metadaten
Author:Jens HüsersORCiD, Maurice MoellekenORCiD, Mats L. RichterORCiD, Mareike PrzysuchaORCiD, Leila Malihi, Dorothee BuschORCiD, Nina-Alexandra GötzORCiD, Jan Heggemann, Guido Hafer, Stefan Wiemeyer, Birgit BabitschORCiD, Gunther Heidemann, Joachim DissemondORCiD, Cornelia Erfurt-BergeORCiD, Ursula Hertha HübnerORCiD
Title (English):An Image Based Object Recognition System for Wound Detection and Classification of Diabetic Foot and Venous Leg Ulcers
URN:urn:nbn:de:bsz:959-opus-35738
DOI:https://doi.org/10.3233/SHTI220397
ISBN:978-1-64368-284-6
ISBN:978-1-64368-285-3
Parent Title (English):Challenges of Trustable AI and Added-Value on Health
Publisher:IOS Press
Place of publication:Amsterdam, Berlin, Washington (DC)
Document Type:Conference Proceeding
Language:English
Year of Completion:2022
Release Date:2022/07/26
First Page:63
Last Page:67
Note:
32nd Medical Informatics Europe Conference (MIE2022), 27.05. - 30.05.2022, Nice (France)
Faculties:Fakultät WiSo
DDC classes:600 Technik, Medizin, angewandte Wissenschaften / 610 Medizin, Gesundheit
Review Status:Veröffentlichte Fassung/Verlagsversion
Licence (German):License LogoCreative Commons - CC BY-NC - Namensnennung - Nicht kommerziell 4.0 International