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Clinically interpretable depression screening via static facial images using deep learning feature extraction and a fine-tuned decision tree | ||
| Health Management & Information Science | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 02 آذر 1404 | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.30476/jhmi.2025.107906.1303 | ||
| نویسندگان | ||
| Khosro Rezaee* 1؛ Hossein Ghayoumi Zadeh2؛ Maryam Saberi Anari3 | ||
| 1Department of Biomedical Engineering, Meybod University, Meybod, Iran | ||
| 2Department of Electrical Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan 7718897111, Iran | ||
| 3Department of Computer Engineering Technical and Vocational University (TVU), Tehran, Iran | ||
| چکیده | ||
| Background: Early and accurate detection of depression remains a pressing clinical challenge, especially in resource-limited environments. Facial expression analysis has emerged as a promising, non-invasive screening method, yet many existing approaches are either computationally intensive or lack clinical interpretability. Objective: This study aims to develop a lightweight, explainable deep learning framework for depression screening using static facial images, with specific focus on clinical relevance and diagnostic transparency. Methods: We propose a hybrid architecture that leverages fine-tuned convolutional features extracted from ResNet-18, followed by classification via a decision tree model optimized using Gini impurity. Facial images were sourced from a publicly available dataset comprising over 20,000 labeled samples, representing diverse adult populations. Images were preprocessed using contrast enhancement and bilateral filtering to preserve subtle affective cues. The model was trained and evaluated using stratified 5-fold cross-validation, with performance assessed via accuracy, precision, recall, F1-score, and confusion matrix analysis. Results: The proposed framework achieved an average classification accuracy of 91.4%, outperforming several baseline visual-only models. Importantly, the use of a fine-tuned decision tree classifier offered clear and interpretable diagnostic rules, aligning with clinical preferences. The model demonstrated robustness across folds and strong generalizability, requiring minimal computational resources. Comparative analysis further highlighted the method's balance between performance and interpretability, making it well-suited for integration into clinical decision support systems. Conclusions: This study demonstrates potential in combining deep learning-based feature extraction with interpretable classifiers for mental health screening. The method offers a practical, explainable, deployable solution for early-stage depression detection using facial imagery. | ||
| کلیدواژه‌ها | ||
| Depression Detection؛ Facial Expression Analysis؛ Deep Learning؛ Classification؛ Clinical Interpretability | ||
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