ANALYSIS OF ACUTE LYMPHOBLASTIC LEUKEMIA CLASSIFICATION WITH MACHINE AND DEEP LEARNING TECHNIQUES

Leukemia is a term for a cancerous condition that affects the organs that produce blood. The inordinate batch of crude white blood cells (WBS) in the bone marrow is the cause of leukemia, a fatal disease. There is a greater possibility of recovery if leukemia is discovered early. In the treatment of leukemia, Deep Learning and Machine Learning algorithms are commonly employed, especially in detecting whether leukemia is progressing in patients. Conventional machine learning and deep learning approaches are practical guides in computer vision that improve the speed and accuracy of identifying and categorizing images (medical), including minuscule blood cells. This survey offers a thorough examination of the recognition and assortment of WBCs and acute leukemia in microscopic blood cells. In this survey, various directions of classification of Acute Lymphoblastic Leukemia (ALL), including the following steps in the methods: Data enhancement, preprocessing, segmentation, extracting features, selecting features(reduction), and classification, with an emphasis on the classification stage. The models using these classifiers have higher performance metrics when compared to the other model. Our suggestion is to offer for identifying and categorizing acute leukemia.