INAP: A Hybrid Approach for NonInvasive Anemia-Polycythemia Detection in the IoMT

Abstract

The paper presents a novel, self-sufficient, Internet of Medical Things-based model called iNAP to address the shortcomings of anemia and polycythemia detection. The proposed model captures eye and fingernail images using a smartphone camera and automatically extracts the conjunctiva and fingernails as the regions of interest. A novel algorithm extracts the dominant color by analyzing color spectroscopy of the extracted portions and accurately predicts blood hemoglobin level. A less than 11.5 gdL \( ^{-1} \) value is categorized as anemia while a greater than 16.5 gdL \( ^{-1} \) value as polycythemia. The model incorporates machine learning and image processing techniques allowing easy smartphone implementation. The model predicts blood hemoglobin to an accuracy of \( \pm \) 0.33 gdL \( ^{-1} \) , a bias of 0.2 gdL \( ^{-1} \) , and a sensitivity of 90 \( \% \) compared to clinically tested results on 99 participants. Furthermore, a novel brightness adjustment algorithm is developed, allowing robustness to a wide illumination range and the type of device used. The proposed IoMT framework allows virtual consultations between physicians and patients, as well as provides overall public health information. The model thereby establishes itself as an authentic and acceptable replacement for invasive and clinically-based hemoglobin tests by leveraging the feature of self-anemia and polycythemia diagnosis.