A Noninvasive Computerized Technique to Detect Anemia Using Images of Eye Conjunctiva

Abstract

Anemia is the blood disorder which develops in the condition of lack of healthy red blood cells or hemoglobin. According to the World Health Organization (WHO) nearly quarter of the human population suffers from anemia moreover, invasive detection of anemia is tedious and expensive. Initial screening for noninvasive detection of anemia is done by examining the color of eye conjunctiva and after that by accommodating the outcomes with an intrusive blood test. This paper aims to resolve this issues along with providing an optimal and fast solution for detecting the anemia using noninvasive methods. This process includes capturing the image of eyes and then manually extracting the eye conjunctiva and obtaining the region of interest (ROI). Once ROI is extracted, these images are processed to obtain the mean intensity values of red and green components of image pixels corresponding to ROI. Then a tuned machine learning algorithm is used to predict whether the patient is anemic or not. The model employed is run over 99 test subjects using k-Fold cross-validation and had achieved an accuracy of 93 percent. This study aims to develop an automated and cost-effective noninvasive technique.