Anemia is a nutritional deficiency highly prevalent among under developed and developing nations. Anemia associated with pregnancy poses severe health/ mortality risks to both mother and infant. Early detection of anemia and treatment is the only solution to reduce the mortality risks. In many developing and under developed countries, early detection is challenging due to low reach and higher cost of invasive diagnostics methods. Towards this end, many non-invasive methods have been developed. These methods use various imaging modalities like eye conjunctiva, fingertip, palm, lip mucosa for detection of anemia. These methods can be briefly categorized to conventional and deep learning methods. Though many techniques have been developed in both categories, the area of non-invasive detection still presents many issues in terms of accuracy, false positives etc. Various challenges in image acquisition, feature engineering, classifier design and optimization etc, need to be addressed to achieve higher accuracy and lower false positives. This work does a critical analysis of existing noninvasive techniques for detection and categorization of anemia. Analysis is done in three categories of image acquisition, feature engineering and classification. The aim is to identify the research gaps and present a solution architecture to address those gaps.
Journal of Theoretical and Applied Information Technology (Mon,) studied this question.