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Classification of acute lymphoblastic leukemia using deep learning

Rehman, A, Abbas, N, Saba, T, Rahman, SIU, Mehmood, Z and Kolivand, H (2018) Classification of acute lymphoblastic leukemia using deep learning. Microscopy Research and Technique, 81 (11). ISSN 1097-0029

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Acute Leukemia is a life-threatening disease common both in children and adults that can lead to death if left untreated. Acute Lymphoblastic Leukemia (ALL) spreads out in children’s bodies rapidly and takes the life within a few weeks. To diagnose ALL, the hematologists perform blood and bone marrow examination. Manual blood testing techniques that have been used since long time are often slow and come out with the less accurate diagnosis. This work improves the diag- nosis of ALL with a computer-aided system, which yields accurate result by using image proces- sing and deep learning techniques. This research proposed a method for the classification of ALL into its subtypes and reactive bone marrow (normal) in stained bone marrow images. A robust segmentation and deep learning techniques with the convolutional neural network are used to train the model on the bone marrow images to achieve accurate classification results. Experimental results thus obtained and compared with the results of other classifiers Naïve Bayesian, KNN, and SVM. Experimental results reveal that the proposed method achieved 97.78% accuracy. The obtained results exhibit that the proposed approach could be used as a tool to diagnose Acute Lymphoblastic Leukemia and its sub-types that will definitely assist pathologists.

Item Type: Article
Additional Information: This is the peer reviewed version of the following article: Rehman, A, Abbas, N, Saba, T, Rahman, Syed Ijaz ur, Mehmood, Z, Kolivand, H. Classification of acute lymphoblastic leukemia using deep learning. Microsc Res Tech. 2018; 81: 1310– 1317. , which has been published in final form at https://dx.doi.org/10.1002/jemt.23139. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
Uncontrolled Keywords: 0299 Other Physical Sciences, 0601 Biochemistry and Cell Biology, 0912 Materials Engineering
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > R Medicine (General)
R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Divisions: Computer Science & Mathematics
Publisher: Wiley
Related URLs:
Date Deposited: 30 Jul 2019 11:10
Last Modified: 04 Sep 2021 09:17
DOI or ID number: 10.1002/jemt.23139
URI: https://researchonline.ljmu.ac.uk/id/eprint/10872
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