Speculative survey of Acute Lymphoblastic Leukemia Classification methods using Blood Smear Microscopic Images

Abstract

Rapid increase in the immature lymphocytic cells leads to Acute Lymphoblastic Leukemia (ALL), which is a type of blood cancer. The challenging task in the classification of ALL is the effective segmentation and the classification of the leukocytes using the Blood Smear Microscopic Images. This survey reviews the research works on the ALL Classification methods, research gaps and the future scope. For the literature review, 20 research papers based on the ALL classification are taken into consideration. The research papers are categorized into Machine learning classifiers, Ensemble classifiers, Deep learning classifiers and so on. The challenges and the research gaps faced during the classification of ALL are elaborated. The result and analysis of the ALL Classification methods are done based on the performance metrics, year of publication and the accuracy range. From the analysis, it is concluded that most of the research works are published in the year 2018. The most commonly used performance metrics is accuracy and the accuracy range for most of the ALL Classification methods ranges from 90% to 94%.