01178nas a2200157   4500000000100000008004100001260000900042100001300051700001100064700001100075700001800086700001600104245009500120300000800215520079700223       2024                            d  bIEEE1 aTurgul S1 aReni S1 aKale I1 aShrivastava J1 aChiodini PL00aA Hybrid Adaptive Object Identification Algorithm for Neglected Tropical Disease Diagnosis  a1-43 a<p>This paper provides an insight into the cross-disciplinary work that combines image processing principles and pathological analysis to diagnose Neglected Tropical Diseases (NTDs). The work described is a novel image processing algorithm that automatically identifies parasitic eggs from Kato-Katz images, in particular, Ascaris and Taenia species. The research outcomes have the potential to be further used for identifying other species that are present in the Kato- Katz images such as Schistosoma Mansoni. The algorithm described in this paper is robust and is able to identify the parasitic eggs at varying levels of magnification and orientation. This algorithm was tested on 4 image sets containing almost 1000 images of Ascaris and Taenia, at lOx and 40x magnification levels.</p>