Deep learning in forensic Analysis: Optical coherence tomography image classification in methamphetamine detection

• Published in: Engineering applications of artificial intelligence Vol. 159; p. 111682
• Main Authors: Gurbuzer, Nilifer, Ozkaya, Alev Lazoglu, Yaylali, Elif Topdagi, Tozoglu, Elif Ozcan, Baygin, Mehmet, Tasci, Burak, Dogan, Sengul, Tuncer, Turker
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 08.11.2025
• Subjects: Digital forensics; Feature engineering; Methamphetamine drug addiction detection; Optical coherence tomography image classification; Methamphetamine drug addiction detection; Digital forensics; Feature engineering; Optical coherence tomography image classification

Detecting drug addiction in forensic science traditionally relies on expensive and time-consuming laboratory tests. This study proposes a rapid, non-invasive approach that uses optical coherence tomography images combined with deep learning techniques to identify methamphetamine users. A novel convo...