A Deep Learning-based System for Detecting Anemia from Eye Conjunctiva Images taken from a Smartphone

Abstract

Anemia is a severe health condition commonly prevalent among women of reproductive age and children below five years. Screening patients before the condition becomes critical and can save many lives. World Health Organization (WHO) has set the “Global nutrition target 2025-anemia,” aiming to reduce 50% of anemia cases among women of reproductive age. This target can be achieved through a time-efficient, cost-effective, and easy-to-use tool. Traditional testing methods require specific chemicals, machines, and equipment that are not available everywhere. It also requires the presence of nurses, laboratory workers, and doctors. These methods are costly, time-consuming, and produce biohazard waste, thus polluting the environment. We developed an Artificial Intelligence (AI)-based bot that can be used for screening people for anemia. The bot service is based on two models: a segmentation model to segment the Region of Interest (ROI) and a classification model to classify anemic cases from normal ones. To train the model, we have collected data from 160 anemic and 140 non-anemic persons. In this paper, we have explained the architecture of the models, all the training parameters, and their deployment on cloud services using the REAN chatbot service. We manage to reach an Intersection Over Union (IOU) score of 0.922 for the segmentation model; validation recall of 0.95 and validation accuracy of 0.9699 for the classification model. This system is easy to use and does not depend on the availability of comprehensive laboratory infrastructure or trained personnel and thus can enable screening of anemia in low-resource settings.