Comparison between a Whole and Separated Feature Information for Acute Lymphoblastic Leukemia (ALL) Classification

Abstract

Acute lymphoblastic Leukemia (ALL) is dangerous cancer in which the infected blood cells disturb the blood and bone marrow. It attacks the body's immune and the ability of bone marrow to produce white blood cells have diminished. This research aims to classify the ALL image using the whole feature information. We proposed a method to decrease the image's size using the whole co-occurrence matrix to represent the object. The research performances have produced 90.77%, 96,67%, and 95.38% for the maximum accuracy, sensitivity, and specificity. This research has also compared to separate channels, which are red, green, and blue. Our novel method shows that the whole feature information has yielded higher accuracy, sensitivity, and specificity than the others, which are the red, green, as well as blue channels. Furthermore, this research has a novelty, i.e., to prove that the whole feature information method is better for the implementation system. Additionally, this research contributes by proposing a method about whole feature information for the implementation system.