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Full-Text Articles in Forensic Biology
Non-Destructive Automated Classification Of Human And Large Mammal Long Bone Fragments: A Deep Learning Approach Using Micro-Ct Histomorphology And Grad-Cam Interpretation, Kathleen Marie Kelley
Non-Destructive Automated Classification Of Human And Large Mammal Long Bone Fragments: A Deep Learning Approach Using Micro-Ct Histomorphology And Grad-Cam Interpretation, Kathleen Marie Kelley
Department of Anthropology: Theses and Student Research
This study explores the development and validation of an automated, deep learning system designed to differentiate human from large mammal long bone fragments using micro-CT imagery. A comprehensive dataset of cross-sectional µCT images was assembled from human and animal skeletal material processed at the DPAA, Nebraska. A convolutional neural network (CNN) based on a ResNet-18 architecture, utilizing transfer learning from ImageNet weights, was trained to classify µCT images as human or animal. The model achieved a mean classification accuracy of 89.85% (± 8.27%) across five sample-level cross-validation folds, with a sensitivity of 96.21% and a 95% bootstrap confidence interval of …