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

作     者：Syed Ijaz Ur Rahman Naveed Abbas Sikandar Ali Muhammad Salman Ahmed Alkhayat Jawad Khan Dildar Hussain Yeong Hyeon Gu 

作者机构：Department of Computer ScienceIslamia CollegePeshawar25120Pakistan Department of Information TechnologyThe University of HaripurHaripur22620Pakistan College of Technical EngineeringThe Islamic UniversityNajaf100986Iraq School of ComputingGachon UniversitySeongnam13120Republic of Korea Department of AI and Data ScienceSejong UniversitySeoul05006Republic of Korea 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模(英文))

年 卷 期：2025年第142卷第2期

页      面：1199-1231页

核心收录：

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

基　　金：supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(RS-2024-00460621 Developing BCI-Based Digital Health Technologies for Mental Illness and Pain Management) 

主　　题：Acute lymphoblastic bone marrow segmentation classification machine learning deep learning convolutional neural network 

摘      要：Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare *** of white blood cells(WBCs)in the blood or bone marrow microscopic slide images play a crucial part in early identification to facilitate medical *** 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 *** 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 *** systematic review has been done in this article under the PRISMA guidelines which presents the most recent advancements in this *** 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 *** 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 ***,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.