AUTOMATED ANAEMIA DETECTION FROM CONJUNCTIVA IMAGES : A MACHINE LEARNING APPROACH FOR ANDROID APPLICATION

Abstract

Anaemia is a common global disorder condition in which the red blood cell count is lower than normal. Traditional diagnostic methods often prove costly, invasive, and inaccessible, leading to delays in treatment and severe consequences. This study explores non-invasive techniques leveraging smartphone technology for efficient anaemia detection. Some researcher investigated that eye’s conjunctiva analysis can a viable alternative, considering its rich blood vessel network and sensitivity to anaemia indicators, independent of skin color. Utilizing smartphone cameras, the study establishes a robust correlation between the color of the conjunctiva and anaemia status, offering a promising avenue for non-invasive diagnosis. Employing a hybrid methodology, the study integrates You Only Look Once (YOLO) version 8 for efficient object detection, achieving a mean average precision of 96% in extracting Regions of Interest (ROI) from conjunctiva images. Subsequently, K-Nearest Neighbors (KNN) classification of features extracted from these ROI’s attained an 83% accuracy rate in determining anaemia status. Furthermore, the study aims to extend its impact by developing an Android application using the Flutter framework. This application integrates the established YOLO and KNN approaches, enabling real-time anaemia detection through smartphone cameras. Such a tool holds the potential to facilitate early evaluations in resource-constrained regions, enabling timely diagnosis and intervention, thus mitigating the adverse effects of untreated anaemia.