Acute Lymboplastic Leukemia Detection Challenges and Systematic Review

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Abstract
-Leukemia is considered as the type of cancer that occurs on the blood cells. It leads to enlargement of the cells and become immature cell, and then it affects the other cells also. In recent times, the testing laboratories have consumed more time to detect as well as to diagnosis the Acute Lymboplastic Leukemia Detection (ALL), which is tedious to detect by humans. Moreover, the manual models and the existing models are also costly and time-consuming too. To overcome from these issues, various Computer Aided Diagnosis (CAD) techniques for analyzing of blood smear microscopic images have been designed by utilizing deep learning and machine learning techniques. Thus, this paper intends to give the literature review for detecting the acute lymboplastic Leukemia with the help of various development approaches. It is also illustrating the wide range of existing studies regarding acute lymboplastic Leukemia detection using machine or deep learning, performance analysis, and its challenges. Since the former methodologies exist with some downside factors, the review paper is helpful for future research work Consequently, the literature review is followed by fundamental prerequisite of acute lymboplastic Leukaemia detection, techniques for forecasting the disease, distinct data sources utilization, analyzing with diverse validating metrics, implementation tools, and also exhibits the information in terms of challenges and motivation towards the future direction.

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