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Classification of acute myelogenous leukemia in blood microscopic images using supervised classifier

机译：使用监督分类器对血微观图像急性髓性白血病的分类

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摘要

Blood cancer is a form of cancer which attacks the blood, bone marrow, or lymphatic system. It is diagnosed with a blood test in which specific types of blood cells are counted by hematologist. We considered only acute myelogenous leukemia, which is one of the blood cancer type which categories under acute leukemia and it mostly comes among adults. Need for automatic diagnosis of leukemia arises when doctors recognize cancers under a microscope which has complete manual work and it's not good for the patient. Automatic diagnosis system which helps hematologists for easier identification and early detection of leukemia from blood microscopic images which will improve the chances of survival for the patient. In this proposed system, which mainly composed of four main stages are preprocessed stage, segmentation stage, feature extraction stage and classification stage respectively. This system framework consists simple and known technique such as K-mean clustering, Local Directional path (LDP), and support vector machine (SVM) respectively. The condition of a patient is shown as normal or abnormal status with the help of classifier. The overall system performance is evaluated using the defined parameters such as sensitivity, specificity, f-measure, and precision which used for calculating the accuracy. Ninety microscopic blood images were tested, and the proposed framework managed to obtain 98% accuracy. Finally, we compare the results of some existing systems with our proposed system to show our achievement on accuracy.
机译：血癌是一种癌症的一种形式，它攻击血液，骨髓或淋巴系统。它被诊断出血液检测，其中特定类型的血细胞被血液学检查计数。我们只考虑急性髓性白血病，这是血癌类型之一，其中急性白血病的类别主要是成年人。当医生在具有完整手动工作的显微镜下识别癌症时，患者的自动诊断都会出现，这对患者不利。自动诊断系统有助于血液学医生更容易识别和早期检测来自血液显微图像的白血病，这将改善患者的生存机动。在该提出的系统中，主要由四个主要阶段组成，分别是预处理的阶段，分割阶段，特征提取阶段和分类阶段。该系统框架分别包括简单和已知的技术，如k均值聚类，局部定向路径（LDP）和支持向量机（SVM）。患者的状况在分类器的帮助下显示为正常或异常状态。使用定义的参数（例如用于计算精度）的定义参数来评估整体系统性能。测试了九十微观血液图像，所提出的框架设施获得98％的精度。最后，我们将一些现有系统的结果与我们提出的系统进行比较，以表明我们的成就。

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