الفهرس 
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Abstract
Diagnosis is performed by a physician to detect the presence or absence of a certain disease in a patient according to a particular dataset, which may include signs, symptoms, medical images, and exams. An incorrect diagnosis can have adverse consequences, for example, prescription of drugs with side effects, on a patient’s health. As well as increasing the costs of treatment, incorrect diagnoses may complicate treatment procedures. To help physicians achieve high diagnostic accuracy, many assistant systems were proposed. Many diseases, including glaucoma, skin cancer, breast cancer, and leukemia, are already addressed by such systems. Early and accurate diagnoses could effectively reduce treatment costs, increase the probability of remission, or even prolong the lives of patients.
Leukemia is a common fatal disease that threatens the lives of many teenagers and children. Infants younger than five years of age are at increased risk. A 2012 study showed that about 352,000 adults and children all over the world develop leukemia, which starts in the bone marrow and is distinguished by the number of white cells increasing in an abnormal manner. This disease has several causes, such as exposure to radiation and certain chemicals, as well as family history. Diagnoses can be performed via a variety of tests, such as physical examination, blood test, blood count, and bone marrow biopsy. Microscopic analysis is considered the most cost-effective procedure for initial diagnoses, but it is usually performed manually by an operator who is vulnerable to fatigue that could result from having to perform many tests in a single day. Moreover, such manual diagnoses are unreliable in themselves, as they are tedious, time-consuming, and subject to inter-observer variations. Hence, there is a need to build automated, low-cost systems that can differentiate between healthy and unhealthy blood smear images with high accuracy but without manual intervention.
Many traditional computer-aided systems use image processing and machine-learning techniques that usually involve several steps, including pre-processing, segmentation, feature extraction, and classification. However, the success of each step depends on the success of the preceding step. For example, the success of classification depends on the success of the preceding feature extraction, which itself depends on the success of the preceding segmentation. Hence, high classification accuracy requires the success of all steps, each of which is non-trivial and problem-dependent