Leukemia Diagnosis using Computational Intelligence

Abstract

Leukemia is a fatal disease that is commonly found in children and also in adults above 55 years of age. It is also known as cancer of blood or bone marrow. [1] It can be categorized into myelogenous leukemia or lymphocytic on the basis of the cells affected by the disease. As the symptoms of the disease are very common like fever, fatigue and body ache, it is not easily detectable at early stages which prove fatal at later stages. So diagnosing it at early stage is crucial for the better prognosis of disease. The paper presents a comparative analysis of extensively used machine learning (ML) algorithm SVM and the relatively new ML algorithm i.e., extreme learning machine for predicting Leukemia. The classification is based on the segmentation of blood smear images publically available dataset ALL-IDB1. The results shows that ELM with an accuracy of 92.2448% outperforms SVM with accuracy 86.3636%.