期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2017
卷号：95
期号：3
出版社：Journal of Theoretical and Applied
摘要：Malaria is one of the public health problems that could cause death, especially in infants, toddlers, pregnant women. Malaria is still a health problem not only in Indonesia but also in some countries in the world. Microscopic testing is the gold standard to malaria disease diagnosis. However, the level of accuracy depends on the level of microbiological expertise and experience. Microscope testing also time consuming and requires extensive equipment. This study built a texture of feature extraction and morphology model that can be used to identify the type of malaria parasites along with the stadium on the image of a thin blood smear. Samples are a number of preparations that have been given indications of malaria and have given a Giemsa staining. Image acquisition process is done by using a digital microscope with 1000 times of magnification. The process of segmentation used thresholding method of Otsu. Selection feature used sequential forward selection (SFS). Classification technique used artificial neural network of learning vector quantization (LVQ) with K-fold cross validation to identify patterns of types of parasites and their life stages in order to get different types and stages of malaria disease. The results used a combination of texture with morphological traits to get the values of accuracy.
关键词：Malaria Disease; Texture Of Feature Extraction And Morphology; SFS; LVQ; K-Fold Validation
