Automated Leukemia Detection and Classification from Blood Smear Images Using Machine Learning Techniques
Y M Manu, M E Priyanka · 2024
Leukemia early detection and identification is a major problem in disease diagnosis because it requires low-cost, precise differentiation of malignant leukocytes in the early stages of the illness. Despite its high frequency, leukemia is difficult to diagnose because of the restricted availability of flow cytometer equipment and the laborious nature of current laboratory diagnostic procedures. Considering machine learning’s (ML) potential for illness diagnosis, the purpose of this systematic review is to look into research that uses ML methods for leukemia detection and classification. Leukemia smear image analysis using machine learning (ML) algorithms can improve diagnostic accuracy, shorten diagnosis times, and provide quicker, safer, and more economical diagnostic services. Clinical and laboratory personnel may also use machine learning techniques into their tools and applications in the lab in addition to their present diagnostic techniques. One method that has been suggested is using a machine learning technique, such artificial neural networks, to diagnose and categorize patients’ conditions early on. As a result after collection of data model development should be done then data training and at last model should be tested and Ml algorithms should be used to compare results. Tests are conducted on samples from blood images.