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Computational and Mathematical Methods in Medicine
Volume 2018, Article ID 6125289, 13 pages
Review Article

Computer-Aided Diagnosis of Acute Lymphoblastic Leukaemia

Department of Computer Science, Bahria University, Islamabad, Pakistan

Correspondence should be addressed to Sarmad Shafique; moc.liamg@50irimhsakdamras and Samabia Tehsin; moc.oohay@aibamast

Received 24 August 2017; Revised 31 December 2017; Accepted 31 January 2018; Published 28 February 2018

Academic Editor: Ruisheng Wang

Copyright © 2018 Sarmad Shafique and Samabia Tehsin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Leukaemia is a form of blood cancer which affects the white blood cells and damages the bone marrow. Usually complete blood count (CBC) and bone marrow aspiration are used to diagnose the acute lymphoblastic leukaemia. It can be a fatal disease if not diagnosed at the earlier stage. In practice, manual microscopic evaluation of stained sample slide is used for diagnosis of leukaemia. But manual diagnostic methods are time-consuming, less accurate, and prone to errors due to various human factors like stress, fatigue, and so forth. Therefore, different automated systems have been proposed to wrestle the glitches in the manual diagnostic methods. In recent past, some computer-aided leukaemia diagnosis methods are presented. These automated systems are fast, reliable, and accurate as compared to manual diagnosis methods. This paper presents review of computer-aided diagnosis systems regarding their methodologies that include enhancement, segmentation, feature extraction, classification, and accuracy.