Automatic Recognition of Acute Lymphoblastic Leukemia Cells in Microscopic Images Using K-Means Clustering and Multiclass Support Vector Machine Classifier

Morteza Moradi Amin, Saeed Kermani, Ardeshir Talebi



Acute lymphoblastic leukemia is the most common form of pediatric cancer which is categorized into three L1, L2, and L3 and could bedetected through screening of blood and bone marrow smears by pathologists. Due to being time‑consuming and tediousness of theprocedure, a computer‑based system is acquired for convenient detection of Acute lymphoblastic leukemia. Microscopic images areacquired from blood and bone marrow smears of patients with Acute lymphoblastic leukemia and normal cases. After applying imagepreprocessing, cells nuclei are segmented by k‑means algorithm. Then geometric and statistical features are extracted from nuclei andfinally these cells are classified to cancerous and noncancerous cells by means of support vector machine classifier with 10‑fold crossvalidation. These cells are also classified into their sub‑types by multi‑Support vector machine classifier. Classifier is evaluated by theseparameters: Sensitivity, specificity, and accuracy which values for cancerous and noncancerous cells 98%, 95%, and 97%, respectively.These parameters are also used for evaluation of cell sub‑types which values in mean 84.3%, 97.3%, and 95.6%, respectively. Theresults show that proposed algorithm could achieve an acceptable performance for the diagnosis of Acute lymphoblastic leukemia ands sub‑types and can be used as an assistant diagnostic tool for pathologists.


Acute lymphoblastic leukemia recognition; hue; saturation; value color space; k-means clustering; multiclass support vector machines classifier; nuclei segmentation

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