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.
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 |
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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): | Creative Commons - CC BY-NC - Namensnennung - Nicht kommerziell 4.0 International |