JARINGAN SARAF TIRUAN LEVENBERG-MARQUARDT UNTUK DETEKSI SEL LIMFOBLAS; LEVENBERG-MARQUARDT ARTIFICIAL NEURAL NETWORK FOR LYMPHOBLAST CELL DETECTION

Pangestuty, Dwi Murdaningsih, Agus Harjoko

2016 | Disertasi | FMIPA

Acute Lymphocytic Leukemia is a type of white blood cell cancers, characterized by the overproduction and accumulation of uncontrolled lymphoblast cells in the bone marrow. Observation of blood cells morphology manually is quite complicated and less effective and efficient, because the process is slow so it takes a long time. Besides that, most of the accuracy depends on subjective factors which are influenced by the experience and expertise, as well as the fatigue factor of a person, this is due to differences in morphological aspects are very subtle between lymphoblast cells and normal lymphocytes. This research aims to develop a method that can be used to detect lymphoblast cells automatically based on microscopic images of blood cells. The experiment will be carried out using 158 images obtained from ALL-IDB2 dataset and have been reverified by the laboratory operator of RSUP Dr. Sardjito Yogyakarta. The approach used in this research is the Otsu thresholding as a method for lymphocytes segmentation and Levenberg-Marquardt ANN as a classifier. After obtaining the main object of lymphocytes, then its features are extracted in order to be trained in the ANN. These features are divided into 2 types of textures and geometry. Texture features are extracted using a gray level co-occurrence matrix (GLCM). Extracted texture features consist of contrast, correlation, energy, entropy, homogeneity, and variance. While the geometry features consist of area, perimeter, compactness, form factor, and radius. The results showed that the performance of Levenberg-Marquardt ANN in detecting lymphoblast cells with an average accuracy of 92.33%, it is not much different when compared with the others, namely SVM classifier with an average accuracy of 93.54%.

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