Bergman, TommyKlöden, MartinDreßler, JanLabudde, Dirk2023-01-182023-01-1820222022http://dx.doi.org/10.1007/s13218-022-00760-yhttps://dl.gi.de/handle/20.500.12116/40043The classification of detected bloodstains into predetermined categories is a crucial component of the so-called bloodstain pattern analysis. 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.BloodClassificationCNNCrime scene reconstructionCriminal eventDeep learningInception v3TensorflowText/Journal Article10.1007/s13218-022-00760-y1610-1987