- 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.
NEAR EAST UNIVERSITY GRAND LIBRARY +90 (392) 223 64 64 Ext:5536. Near East Boulevard, Nicosia, TRNC This software is developed by NEU Library and it is based on Koha OSS
conforms to MARC21 library data transfer rules.