Modeling of the Acute Lymphoblastic Leukemia Detection based on the Principal Object Characteristics of the Color Image

The objective of the research is to develop a model to detect the Acute Lymphoblastic Leukemia. This research contributes a model for Acute Lymphoblastic Leukemia Detection using Principal Object Characteristics of the Color Image. We developed method consist of four main stages as follows: enhancement, segmentation, feature extraction, and accuracy measurement. This research proposed seven characteristics as the object feature, which are Energy (EN), Entropy(EP), Shanon Entropy (H(X)), Log Energy Entropy (EE), Mean (ME), Variance (VA), and Correlation (CO). The feature extraction results have been calculated using four measurement methods, i.e. Euclidean Distance, Manhattan, Canberra, and Chebyshev methods. Our proposed method has produced maximum accuracy of 81.54%, 81.54%, 76.92%, and 82.31% for Euclidean Distance, Manhattan, Canberra, and Chebyshev methods, respectively. Lastly, we applied a confusion matrix to compute the accuracy of the experiment. Our proposed method has been evaluated using Acute Lymphoblastic Leukemia- Image Database (ALL-IDB).