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As in other forensic disciplines, deep learning methods may help to reduce human subjectivity within this process, may increase the classification accuracy, shorten the calculation time and thus, enable high-throughput analysis. In this work, an approach is presented in which a convolutional neural network (Inception v3) was trained from 965 drip stains (passive origin) and 1595 blood spatters (active origin). The trained CNN was evaluated with a test data set consisting of 366 images of drip stains and blood spatters. The success rate was 99.73% which suggests that neural networks could also be used to automatically classify other classes of bloodstain patterns to speed up the investigation process in the future.<\/jats:p>","DOI":"10.1007\/s13218-022-00760-y","type":"journal-article","created":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T08:04:05Z","timestamp":1653033845000},"page":"135-141","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Automatic Classification of Bloodstains with Deep Learning Methods"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5357-4719","authenticated-orcid":false,"given":"Tommy","family":"Bergman","sequence":"first","affiliation":[]},{"given":"Martin","family":"Kl\u00f6den","sequence":"additional","affiliation":[]},{"given":"Jan","family":"Dre\u00dfler","sequence":"additional","affiliation":[]},{"given":"Dirk","family":"Labudde","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,20]]},"reference":[{"key":"760_CR1","unstructured":"Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A. 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