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
• ISSN: 0952-1976
• DOI: 10.1016/j.engappai.2025.111682

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 convolutional neural network was developed, incorporating depthwise and pointwise convolutions, patchify-based downsampling, and inception blocks to improve feature extraction and classification accuracy. To further enhance model performance, we introduced a grid-based deep feature engineering model that extracts and selects discriminative features using iterative neighborhood component analysis. The proposed model achieved 91.02 % accuracy, surpassing the 88.57 % accuracy of Mobile Network version 2 on the same dataset. By integrating the grid-based feature engineering model, classification accuracy was further improved to 93.27 %, demonstrating a significant enhancement over traditional deep learning approaches. The dataset consisted of 2172 optical coherence tomography images collected from 54 methamphetamine users and 60 control subjects, ensuring a diverse and representative sample. This research marks the first application of optical coherence tomography imaging in drug addiction detection, bridging biomedical imaging and forensic science. By employing gradient-weighted class activation mapping visualization, we identified key retinal features that distinguish methamphetamine users from non-users, thereby making the model more interpretable and clinically relevant. Given its high accuracy, lightweight architecture, and non-invasive nature, the proposed method offers a promising forensic tool for rapid, artificial intelligence-driven drug addiction screening with potential real-world applicability in forensic investigations and healthcare. •A new OCT image dataset was collected for detecting methamphetamine use.•We proposed TransformerNeXt, a novel convolutional neural network architecture.•Building upon TransformerNeXt, we developed a deep feature engineering model.•We applied these proposed methods to the collected OCT image dataset to create an automated detection model.•Our models achieved high test classification accuracies, exceeding 90 %.