Automatic acute lymphoblastic leukemia classification model using social spider optimization algorithm

Abstract

The main purpose of this paper is to identify and segment each white blood cells (WBCs) from microscopic images and then classify it to affected or non-affected by acute lymphoblastic leukemia (ALL). The proposed model started by, firstly, detection and isolation of (WBCs). This was performed by conversion of cell images from RGB to CMYK color space. Then histogram equalization followed by thresholding estimation by Zack algorithm was performed for segmentation of cells from the surrounding blood contents. Secondly, some features were extracted from the segmented cells, and they included color, shape, texture and hybrid features. Thirdly, social spider optimization algorithm (SSOA) was applied to select the most appropriate features. Finally, several classifiers were used to validate the performance of the proposed algorithms. The proposed model was applied to the well-known ALL-IDB2 dataset. The results show: firstly, the segmentation (identification and isolations) results were 99.23, 100 and 97.1% of segmentation accuracy, sensitivity and specificity, respectively, which is the highest among other published papers. Secondly, the classification accuracy of the proposed model was higher than the non-SSOA in most experiments. The proposed system achieved remarkable results; 95.23% of classification accuracy with a feature reduction ratio of about 50%, which is the highest among the most recent works on the same dataset.