Analysis of Automated Leukemia Cancer Detection using Feature Selection and Classification Techniques

Leukemia is one of the most dangerous human blood cancers today. Identifying cancer disease in WBC blood microscopic image is complicated as the blood cells contain many dimensions due to high feature evaluation. This research study explores several characteristics of Machine Learning and Deep Learning techniques, focusing on feature selection and classification. The reviews and comparisons show the resultant accuracy of the parameter algorithm in feature selection and classification process by evaluating the testing results in different datasets. Moreover, this study uses other techniques to focus on the problems and identification factors.