Hemoglobin Estimation Techniques: A Survey of Conventional Methods, Clinical Relevance, and Machine Learning Approaches

¹ Research Scholar, Computer Science & Engineering, Brainware University, Barasat, Kolkata – 125, West Bengal, India
² Professor & HOD, Computer Science & Engineering, Brainware University, Barasat, Kolkata – 125, West Bengal, India
³ Associate Professor, Computer Science & Engineering, Brainware University, Barasat, Kolkata – 125, West Bengal, India
⁴ Assistant Professor, Computer Science & Engineering, Brainware University, Barasat, Kolkata – 125, West Bengal, India

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Abstract

Hemoglobin concentration is a critical biomarker for assessing physiological health and diagnosing conditions such as anemia, polycythemia, and chronic diseases. Conventional laboratory-based methods for hemoglobin estimation, including the cyanmethemoglobin method and automated hematology analyzers, provide high accuracy but are invasive, resource-intensive, and often inaccessible in low-resource settings. Recent advances in biomedical engineering have led to the development of non-invasive and point-of-care techniques, such as optical sensing, pulse co-oximetry, smartphone-based imaging, and wearable devices. In parallel, machine learning and deep learning approaches have emerged as powerful tools for estimating hemoglobin levels from physiological signals and medical images. This paper presents a structured and comprehensive survey of hemoglobin measurement techniques, encompassing traditional laboratory methods, emerging non-invasive technologies, and AI-driven approaches. It provides a comparative analysis of these methods in terms of accuracy, cost, portability, and clinical applicability, highlighting key trade-offs and limitations. Furthermore, the paper identifies current challenges, including variability due to physiological and environmental factors, lack of standardization, and limited clinical validation of emerging techniques. The survey em-phasizes the growing potential of hybrid and AI-assisted systems to improve accessibility and scalability of hemoglobin monitoring, particularly in resource-constrained environments. Future research directions are discussed to support the development of reliable, non-invasive, and widely deployable hemoglobin estimation systems.