Deep Learning and Artificial Intelligence-Driven Advanced Methods for Acute Lymphoblastic Leukemia Identification and Classification: A Systematic Review

초록

Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare system. Analysis of white blood cells (WBCs) in the blood or bone marrow microscopic slide images play a crucial part in early identification to facilitate medical experts. For Acute Lymphocytic Leukemia (ALL), the most preferred part of the blood or marrow is to be analyzed by the experts before it spreads in the whole body and the condition becomes worse. The researchers have done a lot of work in this field, to demonstrate a comprehensive analysis few literature reviews have been published focusing on various artificial intelligence-based techniques like machine and deep learning detection of ALL. The systematic review has been done in this article under the PRISMA guidelines which presents the most recent advancements in this field. Different image segmentation techniques were broadly studied and categorized from various online databases like Google Scholar, Science Direct, and PubMed as image processing-based, traditional machine and deep learning-based, and advanced deep learning based models were presented. Convolutional Neural Networks (CNN) based on traditional models and then the recent advancements in CNN used for the classification of ALL into its subtypes. A critical analysis of the existing methods is provided to offer clarity on the current state of the field. Finally, the paper concludes with insights and suggestions for future research, aiming to guide new researchers in the development of advanced automated systems for detecting life-threatening diseases.

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