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Statistical Feature Based Classification of Arthritis in Knee X-Ray Images Using Local Binary Pattern

机译:基于局部二进制模式的膝关节炎关节炎分类的统计特征

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Arthritis is the most common inflammation that occurs in bone joints. The possibility of early disability and joint deformities are high for a person affected by arthritis. By the early diagnosis and treatment of the Arthritis, the damage to the joins can be reduced. A number of therapeutic approaches are now widely available for the diagnosis of this disease. Imaging of the affected joints plays a vital role in the analysis. This paper discuss the classification of arthritis using KNN and Bayesian classifiers based on the feature extracted from digital X-ray images using local binary pattern.
机译:关节炎是骨关节中最常见的炎症。对于受关节炎影响的人来说,早期残疾和关节畸形的可能性很高。通过早期诊断和治疗关节炎,可以减少对加入的损害。现在,许多治疗方法目前可用于诊断这种疾病。受影响的关节的成像在分析中起着至关重要的作用。本文讨论了基于使用局部二进制图案从数字X射线图像提取的特征的knn和贝叶斯分类器进行关节炎的分类。

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