Classification of Acute Myelogenous Leukemia (AML M2 and AML M3) using Momentum Back Propagation from Watershed Distance Transform Segmented Images

International Conference on Computing and Applied Informatics 2016

物理学;计算机科学

Suryani, Esti^1 ; Wiharto^1 ; Palgunadi, Sarngadi^1 ; Nurcahya Pradana, T.P.^1

Ilmu Rekayasa Dan Komputasi Research Group, Sebelas Maret University, Jl. Ir. Sutami No 36, Kentingan Surakarta, Indonesia^1

关键词: Active contour without edges;    Acute myelogenous leukemia;    Characteristic extraction;    Classification process;    Mean and standard deviations;    Momentum back propagation;    Saturation intensity;    Training and testing;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/801/1/012044/pdf
DOI  :  10.1088/1742-6596/801/1/012044

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

This study uses image processing to analyze white blood cell with leukemia indicated that includes the identification, analysis of shapes and sizes, as well as white blood cell count indicated the symptoms of leukemia. A case study in this research was blood cells, from the type of leukemia Acute Myelogenous Leukemia (AML), M2 and M3 in particular. Image processing operations used for segmentation by utilizing the color conversion from RGB (Red, Green dab Blue) to obtain white blood cell candidates. Furthermore, the white blood cells candidates are separated by other cells with active contour without edge. WBC (White Blood Cell) results still have intersected or overlap condition. Watershed distance transform method can separate overlap of WBC. Furthermore, the separation of the nucleus from the cytoplasm using the HSI (Hue Saturation Intensity). The further characteristic extraction process is done by calculating the area WBC, WBC edge, roundness, the ratio of the nucleus, the mean and standard deviation of pixel intensities. The feature extraction results are used for training and testing in determining the classification of AML: M2 and M3 by using the momentum backpropagation algorithm. The classification process is done by testing the numeric data input from the feature extraction results that have been entered in the database. K-Fold validation is used to divide the amount of training data and to test the classification of AML M2 and M3. The experiment results of eight images trials, the result, was 94.285% per cell accuracy and 75% per image accuracy.