A Systematic Review on Acute Leukemia Detection Using Deep Learning Techniques

Abstract

Acute leukemia is a cancer that starts in the bone marrow and is characterized by an abnormal growth of white blood cells. It is a disease that affects people all over the world. Hematologist study blood smears from patients to appropriately diagnose this anomaly. The methods used for diagnosis can be influenced by factors including the hematologist's experience and level of weariness, resulting in nonstandard results and even inaccuracies. The automatic detection of acute leukemia will produce robust results with precise accuracy. This systematic review gives a thorough investigation of the deep learning method for the classification and detection of acute leukemia. The systematic review adopted the PRISMA principle. Four online open source databases were utilized to find comparable articles, and a query featuring relevant keywords was created for the search purpose. Relevant publications were chosen from the search results based on inclusion and exclusion criteria to find answers to the four evolving research questions. The findings of the various studies were examined using the research questions that had been created.F1score and accuracy have been used as a performance matrix for the comparison purpose of CNMC and ALL IDB and self-acquired datasets. Consequently, various challenges faced by the authors have been highlighted. This systematic review article consists of a summary of the various automated detection and classification of acute leukemia in terms of four research questions. Different steps before classification like preprocessing, augmentation, segmentation, and feature extraction with various challenges faced by the author's different datasets and various challenges have been discussed in this paper.