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A Novel Algorithm for the Automatic Detection and Classification of Microcalcification Clusters Using Wavelets

机译:基于小波的微钙化团簇自动检测与分类的新算法

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In the cancerous women society breast cancer can be found to be Second harmful cancer which causes death. According to statistical data the death rates are increasing in every year. Early detection and removal of the cancerous part is the most effective way to heal a cancer which can improve survival rates to a large level. Breast abnormality is find out with the distinguish features which can be simply misconstrue or missed by the radiologist. Again the mammographic screening sensitivity will vary with respect to the quality of image and ability of the radiologist. This means that there are no effective ways for the screening process. By early detecting the presence of micro calcification in the mammograms we can diagnose the breast cancer. This paper is to develop a novel algorithm for distinguishing benign clusters and malignant clusters. The proposed classification system reduces the classification errors and is further proficient in correct diagnosis which will be confirmed by experimental results.
机译:在癌症妇女社会中,乳腺癌是导致死亡的第二种有害癌症。根据统计数据,死亡率每年都在增加。早期发现和切除癌变部分是治愈癌症的最有效方法,可以大幅度提高生存率。可以通过放射科医生简单地误解或遗漏的明显特征来发现乳房异常。乳房X光检查的敏感性也将随着图像质量和放射科医生的能力而变化。这意味着没有有效的筛选过程。通过尽早发现乳房X线照片中微钙化的存在,我们可以诊断出乳腺癌。本文旨在开发一种区分良性和恶性聚类的新算法。提出的分类系统减少了分类错误,并且进一步精通了正确的诊断,这将由实验结果证实。

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