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乳腺癌分类使用通过改进模拟和SVM分类器的特征选择

Maedeh Kiani Sarkaleh¹, Hossein Azgomi¹, Azadeh Kiani-Sarkaleh²

¹Department of Computer Engineering, Ra.C., Islamic Azad University, Rasht 4147654949, Iran.

Diagnostics (Basel, Switzerland)

|February 27, 2026

概括

这项研究引入了一种自动计算机辅助诊断 (CAD) 系统,用于使用乳房影像检测乳腺癌. 该系统使用改进的模拟回火 (ISA) 算法来进行特征选择,显著提高了诊断准确度.

科学领域:

医疗成像医学成像
医疗保健中的人工智能
在瘤学瘤学.

背景情况:

乳腺癌是妇女死亡的主要原因,需要早期检测以改善结果.
计算机辅助诊断 (CAD) 系统对于提高乳房镜分析准确性和加快临床决策至关重要.

研究的目的:

开发一种自动化的CAD系统,用于检测乳房影像中的癌症瘤.
通过先进的特征选择和分类技术,提高乳腺癌诊断的准确性和效率.

主要方法:

一个自动化的CAD系统,包括预处理,特征提取,特征选择 (改进模拟化 - ISA) 和分类 (支持矢量机 - SVM).
预处理涉及ROI提取,降噪和对比度增强.
ISA算法通过复合健身函数自适应地选择了信息特征,以减少维度,同时保持准确性.

主要成果:

该系统在CBIS-DDSM和MIAS数据集上实现了高性能,准确率分别为99.67%和98%.
灵敏度达到99.33%和98%,F1分数分别为99.66%和97.9%.
与传统的模拟和全功能方法相比,ISA算法显示出更高的性能.

结论:

ISA算法在选择乳腺癌检测相关特征方面是有效的.
拟议的CAD系统显著提高了乳房图分析的诊断性能.

关键词:

在SVM分类器中,SVM分类器是:乳腺癌乳腺癌乳腺癌功能选择功能选择图像处理是图像处理的过程.改进了模拟火的模拟火方法.

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