INNOVATIVE AI-BASED TECHNIQUES FOR NON-INVASIVE ANEMIA SCREENING AND DIAGNOSIS

Because many cases of anemia are still found too late, particularly in areas with limited access to laboratorytesting and medical resources, this article developed. Early diagnosis is further hampered by costly testingprocedures and intrusive technologies. To identify anemia, a non-invasive, precise, and efficient technique isneeded. The conjunctival picture of the eye is examined in this study as a non-invasive way to identify anemia.In an effort to effectively classify anemic and healthy patients, a number of model techniques were attempted.The Support Vector Machine (SVM) algorithm-integrated MobileNetV2 method was determined to be the mosteffective plan. With this combination, the accuracy of 93%, sensitivity of 91%, and specificity of 94%. Thesefindings show that the model can successfully identify healthy patients while accurately identifying anemicpatients. This method offers a non-invasive means of detecting anemia early on, making it promising for use inclinical settings. The SVM+MobileNetV2 technique relies on images of the eye’s conjunctiva and canpotentially improve healthcare by identifying people who may have had earlier anemia. This technique standsout as a solid option for the efficient and precise diagnosis of anemia when accuracy, sensitivity, and specificityare balanced.