A Computer-Aided Diagnoses Program for Leukemia Detection Using Blood Samples

Abstract

During the last decades, the number of people given a diagnosis of leukemia had increased significantly. This article aims to automate detection from peripheral blood based on artificial vision methods for more speed and accuracy. The proposed method is applied to data sets from different sources to achieve diversity, which was not pursued before. This required implementation of noise removal, enhancing, and segmentation techniques on the images by applying linear scaling filter, global-local contrast enhancement, and marker-controlled watershed and K-means clustering for each step, respectively. One hundred three features of texture, shape, and color were extracted from cell, nucleus, and cytoplasm subimages, followed by a reduction to 50 most relevant features. The support vector machine model was trained seeking a suitable accuracy to be used in practical experiments. Here, we achieved an accuracy of 91.0256% by implementing the steps previously mentioned in a MATLAB executable program. The software detected leukemia in 5 of 6 images from a new data set, which indicates the usefulness of this approach despite the variation in images.