Classification of Leukemia Detection in Human Blood Sample Based on Microscopic Images

Nowadays, the automatic specific tests such as Cytogenetics, Immunophenotyping and morphological cell classification can identify the leukemia disease by making experienced operators observing blood or bone marrow microscopic images. The early identification of Acute Lymphoblastic Leukemia ALL symptoms in patients can greatly increase the probability of recovery. When typical symptoms appear in normal blood analysis, those methods are not included into large screening programs and are applied only. The method of blood cell observation using Cytogenetics and Immunophenotyping diagnostic methods are currently preferred for their great accuracy with respect to present undesirable drawbacks slowness and it presents a not standardized accuracy since it depends on the operator's capabilities and tiredness. The detection of leukemia in human blood sample using microscopic images is suitable for low costs and remote diagnosis systems. In this paper presents an implementation of detection and classification of leukemia. The system will use features in microscopic images and examine changes on texture, shape and color analysis. Support Vector Machines SVM is used as a classifier, which classifies into cancerous or not. The detection and classification of ALL is implemented with MATLAB programming language. Ei Ei Chaw | Ohnmar Win "Classification of Leukemia Detection in Human Blood Sample Based on Microscopic Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25186.pdf