# Azure-Based Smart Monitoring System for Anemia-Like Pallor
## 2 Related Work  
Anemia-like pallor is a manifestation of the insufficient capacity of oxygen-carrying red blood cells in the body [10]. Although iron deficiency is the primary cause for anemia, other nutritional deficiencies for folate, vitamin B12 and vitamin A, chronic inflammations, parasitic infections and genetic disorders have been found to impact hemoglobin contents in humans [10]. While the onset of anemia can be sudden or chronic, hemoglobin concentrations in individuals have been found to fall below the 95 th percentile of healthy reference populations due to several reasons such as: blood loss, excessive blood cell destruction, or reduced production of hemoglobin [11]. Prolonged and uncontrolled anemia have been observed to lead to manifestations of fatigue, low immunity, insomnia and dementia [12]. Given such situations, the primary objective for diagnosing and monitoring severity of anemia is aimed at preventive treatment and protocol guidance for specialists. Present-day protocols for diagnosis and treatment of anemia include invasive blood draws at pathology labs followed by a complete blood count (CBC) test. This method of invasive diagnostic testing deteriorates the quality-of-life for patients, especially those who need regular monitoring, such as pregnant women and home-bound patients. Several studies evaluating the accuracy of the diagnosis of anemia in physical examinations performed by physicians, medical students and residents, showed no significant difference among examiners, but also showed that the exam was not accurate when anemia is mild to moderate [13]. At present, several anatomical sites are evaluated and the observations are combined in a physical examination to estimate pallor and to maximize the accuracy  
Figure 2: Examples of variations in patient demographics and imaging conditions across pallor site data in the data set under analysis. of the test [13]. Pallor of the conjunctiva (under eye vessels) has been found to be most accurate in the case of beta thalassemia detection with good sensitivity and specificity, regardless of age and gender [14]. This work is aimed to detecting patient pallor caused by anemia-like pathologies from certain focused facial pallor sites, to identify discriminating features for pallor classification tasks without relying on invasive blood draws altogether. One of the primary challenges regarding automated assessment of facial pallor site images involves automated segmentation for facial regions of interest (ROIs) and feature learning for classification tasks. Although facial detection and recognition tasks have been well researched since 1960s [15], automated facial recognition tasks continue to face challenges related to the lack of generalizability owing to gender, ethnicity, age and facial occlusions. Among recent noteworthy efforts, the work in [16] demonstrates the usefulness of luminance and hue-based color planes over red-green-blue (RGB) color planes for skin color identification tasks under non-uniform illumination conditions. Another work in [17] uses fuzzy entropy-based approach for representation of skin-tone colors towards facial detection tasks. Although anemia-like pallor may manifest as pallor of skin color, the primary indicator for pallor is pale/less-flushed blood vessel surfaces. In this work, we analyze skin-independent bio-marker regions for anemia-like pallor using gradient and color-plane based features to indicate the density of flushed blood vessel surfaces. Here, the choice of features for pallor classification is motivated by methods with low time-complexity for feature extraction, which are useful for the development of a fast pallor screening applications. Thus, texture-based features for skin-related segmentation tasks with high computational time complexities such as histogram of oriented gradients (HOG), co-occurrence matrix and Gabor wavelet-based features [18] are not analyzed here.