Non Invasive Anemia Detection in Pregnant Women Based on Digital Image Processing and K-Nearest Neighbor

Anemia is a disease caused by low levels of hemoglobin in the blood. In general, the detection process of anemia is carried out invasively through an examination of hemoglobin levels in the blood. Another method can be done non-invasively by clinical examination of the conjunctiva of the eyes, tongue, palms, and nails. Early detection of anemia is a solution to prevent severe consequences of lack of hemoglobin levels in the blood, especially in pregnant women who are at higher risk of the mother and fetus's safety. This study proposes a non-invasive computer-aided diagnose system for detecting anemia based on digital image processing. The method is by analyzing the conjunctival image of the eye. This study uses the first-order statistic feature extraction method and K-Nearest Neighbor (K-NN) for classifying the conjunctival image into two conditions, anemia and non-anemia conditions. The feature extraction method is performed on RGB, Hue, Saturation, and Value (HSV), and grayscale color space. The system achieved 71.25% of accuracy by using the most optimal parameters on the Green layer of RGB with K=5 and Euclidean distance equation.