Acute leukemia classification by using SVM and K-Means clustering
Jakkrich Laosai, Kosin Chamnongthai · 2014
The proposed system takes as input, Color images of stained peripheral blood smears and identifies the class of each of the White Blood Cells (WBC). The process involves segmentation, feature extraction and classification. Our work focuses on classification of Foil of Bretagne (Lymphoid) and Almeida Lloyd (Myeloid). So that, physicians can analyze, detect anomalies and ensure the diagnosis. The experiment results showed that the performance of identification leukemia using our image processing techniques could classify 100 sample images to Lymphoid stem cells and Myeloid stem cells The method has been evaluated using K-Means clustering. Features extracted from the segmented cytoplasm and nucleus, are motivated by the visual cues of shape and texture. Various classifiers have been explored on different combinations of feature sets. The results presented here are based on trials conducted with normal cells. The highest performance using SVM was of 92%.