CML: Chronic Myeloid Leukemia Detection Using Particle Swarm Optimization and Fuzzy C-Means Clustering

Abstract

Leukemia is a blood cancer caused by a rise in the number of white blood cells in your body. Those white blood cells crowd out the red blood cells and platelets that your body needs to be healthy. The extra white blood cells don’t work right. However, the macroscopic examination of human blood necessitates various microscopic techniques, including color imaging, segmentation, and clustering, to distinguish patients with the disease. The white blood cell count can be higher than normal and prevent the immune system from functioning properly. The most reliable method to detect leukemia is with the assistance of a microscope. Automation of this form of Leukemia diagnosis is required because fewer and more expensive precise tests are required than the above. Blood slides don't reveal the findings because of hematologists' combined experience and tiredness, and those results can vary significantly from one PSO to another. As it is reasonably inexpensive and effective, blood-stained histopathology imaging to test for leukemia is a low-cost and innovative choice. The fuzzy-c segmentation coefficients of this paper are compared to those for image segmentation k. Using the GLCM to separate color characteristics from photographs performs better precision than using Fuzzy-classifications. Fuzzy's consistency level is 96%, while sharpness is 83% CNN is mainly a television outlet. The whole project has been done using the MATLAB toolkit.