{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T09:41:48Z","timestamp":1771062108901,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643683881","type":"print"},{"value":"9781643683898","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T00:00:00Z","timestamp":1684368000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,18]]},"abstract":"<jats:p>For artificial intelligence (AI) based systems to become clinically relevant, they must perform well. Machine Learning (ML) based AI systems require a large amount of labelled training data to achieve this level. In cases of a shortage of such large amounts, Generative Adversarial Networks (GAN) are a standard tool for synthesising artificial training images that can be used to augment the data set. We investigated the quality of synthetic wound images regarding two aspects: (i) improvement of wound-type classification by a Convolutional Neural Network (CNN) and (ii) how realistic such images look to clinical experts (n = 217). Concerning (i), results show a slight classification improvement. However, the connection between classification performance and the size of the artificial data set is still unclear. Regarding (ii), although the GAN could produce highly realistic images, the clinical experts took them for real in only 31% of the cases. It can be concluded that image quality may play a more significant role than data size in improving the CNN-based classification result.<\/jats:p>","DOI":"10.3233\/shti230311","type":"book-chapter","created":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T04:48:52Z","timestamp":1684471732000},"source":"Crossref","is-referenced-by-count":3,"title":["Can Synthetic Images Improve CNN Performance in Wound Image Classification?"],"prefix":"10.3233","author":[{"given":"Leila","family":"Malihi","sequence":"first","affiliation":[{"name":"Institute of Cognitive Science, Osnabr\u00fcck University, Germany"}]},{"given":"Ursula","family":"H\u00fcbner","sequence":"additional","affiliation":[{"name":"Health Informatics Research Group, Osnabr\u00fcck University of AS, Germany"}]},{"given":"Mats L.","family":"Richter","sequence":"additional","affiliation":[{"name":"Institute of Cognitive Science, Osnabr\u00fcck University, Germany"}]},{"given":"Maurice","family":"Moelleken","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Venerology and Allergology, University Hospital of Essen, Germany"}]},{"given":"Mareike","family":"Przysucha","sequence":"additional","affiliation":[{"name":"Health Informatics Research Group, Osnabr\u00fcck University of AS, Germany"}]},{"given":"Dorothee","family":"Busch","sequence":"additional","affiliation":[{"name":"Department of Dermatology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-N\u00fcrnberg, Germany"}]},{"given":"Jan","family":"Heggemann","sequence":"additional","affiliation":[{"name":"Christian Hospital Melle, Niels Stensen Hospitals, Germany"}]},{"given":"Guido","family":"Hafer","sequence":"additional","affiliation":[{"name":"Christian Hospital Melle, Niels Stensen Hospitals, Germany"}]},{"given":"Stefan","family":"Wiemeyer","sequence":"additional","affiliation":[{"name":"Christian Hospital Melle, Niels Stensen Hospitals, Germany"}]},{"given":"Gunther","family":"Heidemann","sequence":"additional","affiliation":[{"name":"Institute of Cognitive Science, Osnabr\u00fcck University, Germany"}]},{"given":"Joachim","family":"Dissemond","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Venerology and Allergology, University Hospital of Essen, Germany"}]},{"given":"Cornelia","family":"Erfurt-Berge","sequence":"additional","affiliation":[{"name":"Department of Dermatology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-N\u00fcrnberg, Germany"}]},{"given":"Carlotta","family":"Barkhau","sequence":"additional","affiliation":[{"name":"Symbic GmbH, Osnabr\u00fcck, Germany"}]},{"given":"Achim","family":"Hendriks","sequence":"additional","affiliation":[{"name":"Symbic GmbH, Osnabr\u00fcck, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3324-9155","authenticated-orcid":false,"given":"Jens","family":"H\u00fcsers","sequence":"additional","affiliation":[{"name":"Health Informatics Research Group, Osnabr\u00fcck University of AS, Germany"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Caring is Sharing \u2013 Exploiting the Value in Data for Health and Innovation"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI230311","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T11:02:48Z","timestamp":1685530968000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI230311"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,18]]},"ISBN":["9781643683881","9781643683898"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti230311","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,18]]}}}