Detection of anemia using conjunctiva images: A smartphone application approach

Anemia is one of the public health issues that affect children and pregnant women globally. Anemia occurs when the level of red blood cells within the body is reduced. Detecting anemia requires expert blood draw for clinical analysis of hemoglobin quantity. Although this standard method is accurate, it is costive and consumes enough time, unlike the non-invasive approach which is cost-effective and takes less time. This study focused on pallor analysis and used images of the conjunctiva of the eyes to detect anemia using machine learning techniques. This study used a publicly available dataset of 710 images of the conjunctiva of the eyes acquired with a unique tool that eliminates any interference from ambient light. We combined Convolutional Neural Networks, Logistic Regression, and Gaussian Blur algorithm to develop a conjunctiva detection model and an anemia detection model which runs on a Fast API server connected to a frontend mobile app built with React Native. The developed model was embedded into a smartphone application that can detect anemia by capturing and processing a patient's conjunctiva with a sensitivity of 90%, a specificity of 95%, and an accuracy of 92.50% on average performance in about 50 s.