Detection of acute lymphocyte leukemia using k-nearest neighbor algorithm based on shape and histogram features

International Conference on Physical Instrumentation and Advanced Materials

物理学;材料科学

Purwanti, Endah^1 ; Calista, Evelyn^1

Biomedical Engineering, Faculty of Science and Technology, Universitas Airlangga, Kampus C, Mulyorejo, Surabaya

60115, Indonesia^1

关键词: Automatic Detection;    Classification algorithm;    Combined features;    Histogram features;    K nearest neighbor algorithm;    K-nearest neighbours;    Mean standard deviation;    Peripheral blood smears;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/853/1/012011/pdf
DOI  :  10.1088/1742-6596/853/1/012011

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

Leukemia is a type of cancer which is caused by malignant neoplasms in leukocyte cells. Leukemia disease which can cause death quickly enough for the sufferer is a type of acute lymphocyte leukemia (ALL). In this study, we propose automatic detection of lymphocyte leukemia through classification of lymphocyte cell images obtained from peripheral blood smear single cell. There are two main objectives in this study. The first is to extract featuring cells. The second objective is to classify the lymphocyte cells into two classes, namely normal and abnormal lymphocytes. In conducting this study, we use combination of shape feature and histogram feature, and the classification algorithm is k-nearest Neighbour with k variation is 1, 3, 5, 7, 9, 11, 13, and 15. The best level of accuracy, sensitivity, and specificity in this study are 90%, 90%, and 90%, and they were obtained from combined features of area-perimeter-mean-standard deviation with k=7.