Cantürk, İsmailÖzyılmaz, Lale2024-10-182024-10-182024Cantürk, İ., & Özyılmaz, L. (2024). A deep feature driven expert system to estimate the postmortem ınterval from corneal opacity development. Expert Systems. https://doi.org/10.1111/exsy.137571468-03940266-4720https://doi.org/10.1111/exsy.13757https://hdl.handle.net/20.500.13055/827Postmortem interval (PMI) estimation remains an unresolved challenge in forensic science, necessitating practical, reliable andmore accurate tools. This study aimed to develop a quantitative PMI estimation tool that effectively meets these needs. Focusingon the postmortem opacity development of the eye as a key marker for determining time since death, we propose an artificialintelligence-based clinical PMI prediction system utilising computer vision, deep learning and machine learning methods. TheAlexNet algorithm was utilised to extract deep features from the postmortem eye images. Extracted features were then processedby machine learning algorithms. For feature selection, Lasso and Relief techniques were employed, while SVM and KNN wereapplied for classifications. The results were validated using the leave- one-subject-out method. The system was tested across dif-ferent postmortem ranges, providing multi-label predictions. The performance was evaluated using various metrics. The deepfeatures exhibited effective performance in grading postmortem opacity development, achieving state-of-the-art results. Theaccuracy scores were 0.96 and 0.97 for 3-h intervals (i.e., 5-class) and 5-h intervals (i.e., 3-class) experiments, respectively. Theexperimental results indicate that the proposed system represents a promising tool for PMI estimation.eninfo:eu-repo/semantics/closedAccessCorneal OpacityDeep FeaturesForensic SciencePostmortem Interval EstimationTime of DeathArticle10.1111/exsy.13757Q2WOS:0013302675000012-s2.0-85206153436Q1