Breaking Boundaries in Diagnosis: Non-Invasive Anemia Detection Empowered by AI

Abstract

This article evolved because several instances of anemia are still discovered too late, especially in communities with limited medical resources and access to laboratory tests. Invasive diagnostic technologies and expensive expenses are additional impediments to early diagnosis. An effective, accurate, and non-invasive method is required to detect anemia. In this study, the conjunctival image of the eye is analyzed as a non-invasive method of detecting anemia. Various model approaches were tested in an endeavor to categorize anemic and healthy patients as accurately as possible. The Support Vector Machine (SVM) algorithm-integrated MobileNetV2 method was determined to be the most effective plan. With this combination, the accuracy of 93%, sensitivity of 91%, and specificity of 94%. These findings show that the model can successfully identify healthy patients while accurately identifying anemic patients. This method offers a non-invasive means of detecting anemia early on, making it promising for use in clinical settings. The SVM+MobileNetV2 technique relies on images of the eye’s conjunctiva and can potentially improve healthcare by identifying people who may have had earlier anemia. This technique stands out as a solid option for the efficient and precise diagnosis of anemia when accuracy, sensitivity, and specificity are balanced.

Keywords

• Anemia detection
• eye conjunctival images
• MobileNetV2
• non-invasive
• SVM