Leukemia Cancer Cells Segmentation and Classification Using Machine Learning

Abstract

Human deaths can be attributed to leukaemia, a form of cancer. The survival rates of those treated for it only improve with better detection and diagnosis. Currently, pathologists examine microscopic images for signs of cancer or blood problems. To achieve this, we look at the images' texture, geometry, colour, and statistical analysis for differences. This study details a variety of feature extraction strategies for identifying leukaemia in micrographs of blood samples. The use of image analysis is crucial to this process. Here, we begin with a discussion of fundamentals in cell biology and proceed to demonstrate our proposed method in action. In an effort to keep our prices as low as possible, we are just making use of visuals. We have been using MATLAB as a cancer cell detecting tool.