An Extensive Study of Different Types of Leukemia using Image Processing Techniques
Keywords:
Leukemia, Machine Learning, White Blood Cells, Blood Smear Images, Bone Marrow, Chronic LeukemiaAbstract
The use of computer-aided diagnosis (CAD) has been on the rise during the last few years. Different diseases, such as leukaemia, can be detected using a variety of machine learning methods. The bone marrow and/or blood are both affected by leukaemia, which is a Sickness caused by WBCs. Detection of leukaemia at an early stage is essential to the patient's recovery and prognosis. The two primary forms of leukaemia that have evolved as a result of scientific developments are acute and chronic leukaemia. Myeloid and lymphoid cells are subtypes of any given sort. As a result, four separate types of leukaemia exist. A variety of strategies can be used to identify leukaemia subtypes. In spite of this, there has yet to be a complete examination of these strategies. Such a review will be quite helpful to those who are just starting out in this field of study and want to learn more. To fill the hole, this publication provides an extensive overview of previous studies on blood smear image analysis and leukaemia detection. For the most part, the review concentrates on discussing the underlying procedures and their claimed results. It also lists the problems in the field that have been resolved and the outstanding ones that remain.
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