Gurbuzer, NiliferOzkaya, Alev LazogluYaylali, Elif TopdagiTozoglu, Elif OzcanBaygin, MehmetTasci, BurakTuncer, Turker2026-03-262026-03-2620250952-19761873-676910.1016/j.engappai.2025.1116822-s2.0-105009845113https://doi.org/10.1016/j.engappai.2025.111682https://hdl.handle.net/20.500.14901/2987Tasci, Burak/0000-0002-4490-0946; Ozkaya, Alev/0000-0002-2033-3692Detecting 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.eninfo:eu-repo/semantics/closedAccessMethamphetamine Drug Addiction DetectionFeature EngineeringOptical Coherence Tomography ImageClassificationDigital ForensicsArticle