A deep feature driven expert system to estimate the postmortem interval from corneal opacity development
Yükleniyor...
Dosyalar
Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Wiley
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Postmortem 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.
Açıklama
Anahtar Kelimeler
Corneal Opacity, Deep Features, Forensic Science, Postmortem Interval Estimation, Time of Death
Kaynak
Expert Systems
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
Sayı
Künye
Cantürk, 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.13757
