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

Endah Purwanti, Evelyn Calista

Research output: Contribution to journalConference articlepeer-review

17 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number012011
JournalJournal of Physics: Conference Series
Volume853
Issue number1
DOIs
Publication statusPublished - 7 Jun 2017
EventInternational Conference on Physical Instrumentation and Advanced Materials, ICPIAM 2016 - Surabaya, Indonesia
Duration: 27 Oct 2016 → …

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