High-precision hemoglobin detection based on hyperspectral reconstruction of RGB images

Hemoglobin concentration is an important indicator for evaluating human health. The concentration value is an important evidence for early diagnosis of anemia and other blood diseases. However, existing optical detection methods usually suffer from low precision, strong noise and tedious operation, makes it difficult to obtain an accurate hemoglobin concentration evaluation. In this work, we propose and demonstrate a method for hemoglobin detection with the hyperspectral reconstruction of RGB images. To be specific, this overall model consists of a SHR-Net (Skin Hyperspectral Reflectance Network) to reconstruct hyperspectral images from RGB images of fingers, a feature selection process to capture full-band reflectance spectra based on the maximum blood perfusion region and an SVR (Support Vector Regression) algorithm to predict the hemoglobin concentration. The experimental results show that the coefficient of determination between the predicted and true values is 0.945. The mean square error of cross-validation is 0.49 g/dL. Therefore, this method provides a new solution for the accurate and noninvasive measurement of hemoglobin.