Automated Facial Pain Assessment Using Dual-Attention CNN with Medical-Grade Calibration and Reproducibility Framework

Accurate and quantitative pain assessment remains a major challenge in clinical medicine, especially for patients unable to verbalize discomfort. Conventional methods based on self-reports or clinician observation are subjective and inconsistent. This study intro-duces a novel automated facial pain assessment framework built on a dual-attention Convolutional Neural Network (CNN) that achieves Medical-Grade reliability and inter-pretability. The architecture combines multi-head spatial attention to localize pain-relevant facial regions with an enhanced channel-attention block employing triple pooling (average, maximum, and standard-deviation) to capture discriminative intensity features. Regularization through label smoothing (α = 0.1) and AdamW optimization ensures calibrated, stable convergence. Evaluated on a clinically annotated dataset using subject wise stratified sampling, the proposed model achieved a test accuracy of 90.19 % ± 0.94 %, with an average 5-fold cross-validation accuracy of 83.60 % ± 1.55 %. The model further attained an F1-score of 0.90 and Cohen’s κ = 0.876, with macro and micro-AUCs of 0.991 and 0.992, respectively. The evaluation covers five pain classes (No Pain, Mid Pain, Moderate Pain, Severe Pain, and Very Pain) using subject-wise splits comprising 5,840 total images and 1160 test samples. Comparative benchmarking and ablation experiments confirm each module’s contribution, while Grad-CAM visualizations highlight physio-logically relevant facial regions. The results demonstrate a robust, explainable, and re-producible framework suitable for integration into real-world automated pain-monitoring systems.