首页> 外文会议>International Symposium on Instrumentation Measurement, Sensor Network and Automation;IMSNA >Malignant tumor detection using linear support vector machine in breast cancer based on new optimization algorithms
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Malignant tumor detection using linear support vector machine in breast cancer based on new optimization algorithms

机译:基于新优化算法的线性支持向量机在乳腺癌中的恶性肿瘤检测

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Breast cancer is one of the most common fatal diseases in women. Early detection of malignant breast cancer could be a great help in treating this cancer. Many studies have been performed in order to detect the malignant of cancer tumor till now. It has been tried to contribute more in accurate diagnosis of breast cancer by Support Vector Machine, in this paper. LS and SMO methods have been utilized instead of conventional learning method of QP in SVM in this probe. The feasibility of 100 percent in sensitivity for LS-SVM, and 100 percent in specificity for SMO-SVM has been achieved in this assay by the proposed method, which this percentage has not been achieved so far in the previous studies. The highest value among the previous studies has been presented by the obtained accuracy in LS-SVM method.
机译:乳腺癌是女性最常见的致命疾病之一。早期发现恶性乳腺癌可能对治疗这种癌症有很大帮助。迄今为止,已经进行了许多研究以检测癌症肿瘤的恶性。本文尝试通过支持向量机为乳腺癌的准确诊断做出更多贡献。在此探针中,已使用LS和SMO方法代替了SVM中QP的常规学习方法。 LS-SVM的敏感性为100%,SMO-SVM的特异性为100%的可行性已通过所提出的方法在该测定法中实现,而迄今为止在先前的研究中尚未达到该百分比。 LS-SVM方法获得的精度已显示出先前研究中的最高值。

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