survey

Automated Analysis of Blood Smear Images for Leukemia Detection: A Comprehensive Review

Authors:

Sabrina Dhalla,

Aastha GuptaAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 11s

Article No.: 234, Pages 1 - 37

https://doi.org/10.1145/3514495

Published: 09 September 2022 Publication History

Abstract

Leukemia, the malignancy of blood-forming tissues, becomes fatal if not detected in the early stages. It is detected through a blood smear test that involves the morphological analysis of the stained blood slide. The manual microscopic examination of slides is tedious, time-consuming, error-prone, and subject to inter-observer and intra-observer bias. Several computerized methods to automate this task have been developed to alleviate these problems during the past few years. However, no exclusive comprehensive review of these methods has been presented to date. Such a review shall be highly beneficial for novice readers interested in pursuing research in this domain. This article fills the void by presenting a comprehensive review of 149 papers detailing the methods used to analyze blood smear images and detect leukemia. The primary focus of the review is on presenting the underlying techniques used and their reported performance, along with their merits and demerits. It also enumerates the research issues that have been satisfactorily solved and open challenges still existing in the domain.

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Index Terms

Automated Analysis of Blood Smear Images for Leukemia Detection: A Comprehensive Review
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning

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ACM Computing Surveys Volume 54, Issue 11s

January 2022

785 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3551650

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Published: 09 September 2022

Online AM: 01 March 2022

Accepted: 01 January 2022

Revised: 01 October 2021

Received: 01 December 2020

Published in CSUR Volume 54, Issue 11s

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