Acute Lymphoblastic Leukemia Prediction Using Covering-oriented Rough K-means Segmentation Algorithm

Acute Lymphoblastic Leukemia (ALL) is a type of dreadful cancer that has an effect on the bone marrow and blood. In recent decades, leukemia mainly affects youngsters and grown-ups. Timely diagnosis of leukemia is crucial to give right treatment to patients, predominantly in the case of children. Computational tecniques for medical image analysis have incredible use and become the focus of research in medical image processing. The major objective of this research work is to predict the acute lymphoblastic leukemia cells using the proposed segmentation algorithms with the assistance of machine learning classifiers. In this study, hybrid clustering based segmentation methods Covering-orientedRough K-Means (CRKM) algorithm is proposed to segment the image of the leukemia nucleus. Then, the performance of the segmentation algorithms were analyzed using different machine learning classifiers. From the experimental analysis, it is observed that the machine learning classification algorithms achieved better prediction accuracy when compared to existing segmentation algorithms