An In-Depth Assessment and Comparison of Learning Methods for Non-Invasive Anemia Identification

Abstract

Anemia, caused by a reduced number of red blood cells or changes in cell structure, poses a significant health issue globally, particularly for children and pregnant women. Clinical diagnosis can be obstructed by patient hesitation, a lack of healthcare personnel in remote areas, and limited resources. However, due to its accessibility, affordability, and non-invasive nature, machine learning offers a compelling alternative. This paper methodically evaluates current applications of machine learning in the diagnosis of anemia. We analyze prominent algorithms, the characteristics of datasets, performance metrics, and techniques for image augmentation. Our findings illustrate the potential of non-invasive, machine learning-based methods for effective and cost-efficient screening of anemia. This paper highlights the capabilities of machine and deep learning in assessing clinical data and medical imagery to enhance anemia detection.