Deep multi-modal features based spatio-temporal video regression for non-invasive hemoglobin estimation
Medical and Biological Engineering and Computing, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s11517-026-03618-9
- Dergi Adı: Medical and Biological Engineering and Computing
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, BIOSIS, CINAHL, Compendex, EMBASE, INSPEC, MEDLINE, Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Biomedical Reference Collection: Corporate Edition (EBSCO), Business Source Ultimate (EBSCO), Engineering Source (EBSCO), Health Research Premium Collection (ProQuest), Pharma Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: Anemia diagnosis, Computer vision, Deep learning, Hemoglobin concentration estimation, Machine learning, Non-invasive measurement, Remote photoplethysmography, Video regression
- Yıldız Teknik Üniversitesi Adresli: Evet
Özet
Abstract: The hemoglobin concentration in blood is vital for diagnosing anemia and monitoring the various health conditions. However, conventional measurement methods need invasive blood sampling so that they might have limited accessibility and uncomfortable for patients. Today, non-invasive alternatives powered by machine learning techniques provide promising solutions for point-of-care facilities and remote healthcare systems. This paper presents a methodology through a comprehensive research and development process to estimate hemoglobin levels from facial videos using multi-modal feature extraction and ensemble learning techniques. A dataset of 260 participants with various blood hemoglobin levels was processed to extract the features from pre-trained convolutional neural-networks (MobileNetV2, ResNet152), remote photoplethysmography (rPPG) signals, and color statistical features. Using these features, hemoglobin concentration was estimated via a number of machine learning models including XGBoost, Random Forest, and Stacking Regressor, respectively. Stacking Regressor provided the best estimation scores with a mean-absolute error of 0.7754 g/dL, Pearson correlation-coefficient of 0.7878, and score of 0.5852. ResNet152 model based features were combined with XGBoost, which achieved comparable performance (MAE: 0.6635 g/dL, : 0.4977). Experimental results demonstrated that multi-modal feature strategy outperformed single-modality approaches in terms of prediction accuracy and robustness. The proposed video-based estimation of hemoglobin concentration system achieves clinically relevant accuracy levels, outperforms to literature methods, comparable to point-of-care instruments demonstrating strong potential for use in anemia screening and remote patient monitoring.