Random Forest Classifier-Based Acute Lymphoblastic Leukemia Detection from Microscopic Blood Smear Images

Acute lymphoblastic leukemia (ALL) is a cancerous condition which affects bone marrow and blood. It is a fast developing illness that, if not identified and treated as soon as possible, could be fatal. ALL is often identified by hematologists through observing the blood and bone marrow smears under a microscope. In order to diagnose and classify leukemia, sophisticated cytochemical tests are employed. However, such processes are resource-intensive, time-consuming, and reliant on the expertise of the doctors doing them. In order to diagnose leukemia, image processing techniques are used to examine microscopic smear images for signs of cancerous cells. These methods are simple, quick, cheap, and not influenced by the views of specialists. In this paper, a computer-aided automated diagnostic method is proposed to classify ALL and healthy cells based on Random Forest classifier with most significant features. For this model, the public dataset ALL-IDB 2 has been utilized. The proposed approach provided an accuracy of 99.73% to classify the cells (ALL and healthy). Also, it shows an improvement in accuracy of 6.16%, 16.4%, and 10.43% in comparison to the approaches, i.e., morphological + color feature with SVM, Hausdorff dimension + shape feature with SVM, and GLCM + Morphological with SVM, respectively.