Identification and Classification of Leukemia Using Machine Learning Approaches

Leukemia is a form of cancer that affects people of any age but is particularly frequently seen in kids aged between the ages of five and eight. One of two methods is typically used for diagnosing it. The first approach is the traditional method of manually examining samples of blood extracted from microscope pictures; however, this method has a drawback in that when leukemia cells and healthy ones are the same, the diagnosis procedure gets more complicated. The second method employs image categorization algorithms. Algorithms employed in data mining techniques are extremely important in the field of health care, particularly in the event of discovering previously unknown patterns or models in databases. The classification is becoming more popular in Machine Learning and Deep Learning methods. The purpose of this research is to examine ML and DL approaches for recognizing and forecasting leukemia. The present research attempts to uncover several ML and DL-based classification methods that exploit the greatest prediction accuracy for leukemia cancer cells in added to the existing methods.