الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Leukemia is a type of cancer caused by abnormal increase of the white blood cells. Each year hundreds of thousands of people die of leukemia throughout the world. Leukemic cells become out of control and they spread independently. They cause structural and irreversible damage in the organs where blood cells are produced, in other organs and in tissues. If not treated properly, leukemia costs the life of the patient very quickly. Early diagnosis is vital and it should be followed by a treatment applied to the correct cells. It is possible to achieve successful results in treatment, if leukemic and non-leukemic cells are classified correctly. This research work is focused on acute lymphocytic leukemia, which affects young children and has a higher expectation of survival rate as compared to acute myelogenous leukemia. A multi step process is developed, consisting of the extraction of a region of interest from ALL microscopic images and by using C-Y color model . The images are segmented and set of unique features are extracted based on cell color, size and nuclear morphological information. In the classification step, a Multi Layer Perceptron (MLP) neural network classifier is applied and its resulted are compared with the result of support vector machine classifier (SVM). The performance of the two classifiers was compared using the extracted feature set to determine which could achieve the highest accuracy on a common data set extracted from 108 images of Acute Lymphoblastic Leukemia (ALL). The MLP algorithm’s performance is 93.58 %.