A Reproducible Mobile-Cloud Framework for Preliminary Non-Invasive Anemia Screening from Palpebral Conjunctiva Images

Huamaní E.L.; Montufar-Barrientos E.; Romero-Alva V.; Velasquez-Jimenez L. · International Journal of Advanced Computer Science and Applications (2026) · DOI: 10.14569/ijacsa.2026.0170804

Resumen

—Non-invasive anemia screening from palpebral conjunctiva images may support preliminary triage when access to laboratory hemoglobin testing is limited. This study presents and evaluates a reproducible mobile-cloud framework that integrates guided image capture, automated eye-region localization, region-of-interest extraction, color normalization, image-quality control, deep-learning inference, probability calibration, longitudinal storage, and user feedback. The evaluation used five-fold image-level cross-validation, a locked hold-out image set, preprocessing ablation, mobile efficiency benchmarking, and a formative usability assessment. Among the evaluated backbones, EfficientNetB0 achieved a cross-validated accuracy of 87.84%±0.94, sensitivity of 89.20%±1.38, specificity of 86.78%±0.72, F1-score of 86.68%±0.77, and area under the receiver operating characteristic curve of 0.927±0.007. On the locked hold-out set of 120 images, the model achieved 85.83% accuracy, 87.93% sensitivity, 83.87% specificity, 83.61% precision, 85.71% F1-score, and 0.952 AUC. The ablation analysis showed incremental gains from automated ROI extraction, white balancing, contrast normalization, and image-quality rejection. The results indicate that predictive performance, calibration, acquisition quality, and deployment cost should be evaluated together when designing mobile screening support systems. The tool is intended for preliminary screening and does not replace laboratory confirmation. © (2026), (Science and Information Organization). All rights reserved.