Abnormality Classification on the Shape of Red Blood Cells using Radial Basis Function Network

When diagnosing a disease, besides the physical examination, blood analysis is a reliable method. This because blood has components that contains a lot of key informations. Morphogical examination of peripheral blood smears is one of important lab examinations and has to be evaluated properly. But abnormal red blood cell shapes that found by a health analyst is not always the same as other analyst because of precision factor, concentration, and lack of knowledge. Besides that, morphogical examination of peripheral blood smears still done manually by health anaylists and they considered less efficient because they took a lot of time. To solve that problem, a method to classify red blood cell types that detects abnormal shapes of cells from certain disease. In this paper, radial basis function network is used as method to classify abnormal red blood cell types. Several stage before executing classification process is input image, pre-processing, feature extract with canny edge detection. Research result shows that by using this method, the accuracy to classify abnormal red blood cell types is 83.3%