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癌症检测和分类使用简化的二进制状态矢量机器

Imran Shafi¹, Sana Ansari¹, Sadia Din²

¹College of Electrical & Mechanical Engineering, National University of Science and Technology, Islamabad, Pakistan.

Medical & biological engineering & computing

|February 1, 2024

概括

本研究引入了状态向量机 (SVM) 方法,用于准确的癌症检测和分类. 该SVM方法实现了94.90%的准确性,优于其他机器学习技术和医生,改善了早期诊断.

科学领域:

在瘤学瘤学.
机器学习机器学习
生物医学信息学生物医学信息学

背景情况:

癌症的高死亡率需要改进早期检测方法.
准确的瘤分类 (良性与恶性) 对于有效的治疗计划至关重要.
现有的诊断方法可以通过先进的计算方法来增强.

研究的目的:

开发和评估一种有效的机器学习方法,用于癌症诊断和分类.
将状态向量机 (SVM) 的性能与神经网络架构和人类专家进行比较.
使用淋巴图数据调查癌症检测中SVM分类器的最佳参数.

主要方法:

利用在线淋巴图数据用于训练和测试机器学习模型.
实现和优化状态向量机 (SVM) 分类器.
将SVM性能与前和通用回归神经网络进行比较.
预处理的数据包括消除噪音和功能优化.

主要成果:

提出的基于SVM的方法在早期癌症检测和分类方面表现出卓越的表现.
两类SVM的准确性最高,达到94.90%,超过其他分类器.
SVM方法被证明是稳健的,能够为复杂的任务划分分类别.
SVM分类的准确性超过了经验丰富的医生和其他机器学习方法的准确性.

结论:

状态矢量机 (SVM) 是用于准确诊断和分类癌症的高效工具.
开发的SVM方法显著改善了现有的早期癌症检测方法.
这种机器学习策略为分类瘤和帮助临床决策提供了强大而准确的解决方案.

关键词:

反向繁殖是一种反向传播.癌症检测癌症检测一般化的回归研究.恶性淋巴细胞的恶性淋巴发生转移的转移.支持矢量机器的支持矢量机器.

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