Acute lymphoblastic leukemia identification using blood smear images and a neural classifier

Authors:
Adnan Khashman;Hayder Hassan Abbas
Affiliations:
Intelligent Systems Research Centre (ISRC), Near East University, Lefkosa, Turkey;Intelligent Systems Research Centre (ISRC), Near East University, Lefkosa, Turkey,Department of Electrical and Electronic Engineering, Near East University, Lefkosa, Turkey
Venue:
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
Year:
2013

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Abstract

There is a need for fast and cost-effective leukemia identification methods, because early identification could increase the likelihood of recovery. Currently, diagnostic methods require sophisticated expensive laboratories such as immune-phenotype and cytogenetic abnormality. Therefore, we propose an identification method based on using blood smear images of normal and cancerous cells, in addition to a neural network classifier. We focus in this paper on identifying Acute Lumphoblastic Leukemia (ALL) cases, and implement our experiments following three learning schemes for a neural model. The neural classifiers distinguish between normal blood cells and ALL-infected cells. The experimental results show that the proposed novel leukemia identification system can be effectively used for such a task, and thus could be implemented for identifying other leukemia types in real life applications.