一个高效的乳腺癌分类模型,使用双边过和模糊卷积神经网络
A Abdul Hayum1, J Jaya2, R Sivakumar3
1Electronics and Communication Engineering, Hindusthan Institute of Technology, Coimbatore, 641032, India. aabdulhayum@gmail.com.
Scientific reports
|March 16, 2024
概括
这项研究引入了一种改进的乳腺癌检测模型,使用先进的图像处理和机器学习技术. 新方法提高了乳腺癌亚型分类的准确性和效率,优于以前的方法.
科学领域:
- 医疗成像医学成像
- 计算生物学 计算生物学
- 人工智能的人工智能
背景情况:
- 乳腺癌 (BC) 仍然是女性癌症死亡的主要原因.
- 之前的BC分类模型使用了中位数过器,加权K-Means集群 (WKM) 和改进的Cuckoo搜索优化 (ICSO) 与修改的循环神经网络 (MRNN).
- 现有的方法面临挑战,包括缓慢的搜索速度,低收精度和MRNN培训中的困难.
研究的目的:
- 开发一个更准确,更有效的乳腺癌检测和分类模型.
- 为了克服以前模型的局限性,特别是缓慢的优化和复杂的培训程序.
- 增强图像预处理,特征提取和分类阶段,以提高诊断性能.
主要方法:
- 使用双边过和对比度拉伸进行图像预处理.
- 通过修改的模糊C-平均 (MFCM) 集群来对兴趣区域 (ROI) 的细分.
- 使用基于中心距离函数 (CDF) 和基于对角纹理矩阵 (CDTM) 的方法进行特征提取,包括颜色直方图和形状描述器.
- 使用内核主要组件分析 (KPCA) 减少尺寸.
- 使用突变群优化 (MCSO) 进行特征选择.
- 使用模糊卷积神经网络 (FCNN) 使用MCSO优化的参数进行分类.
主要成果:
- 拟议的模型实现了高精度,回忆和f-测量值.
- 实验结果表明,与现有的乳腺癌分类模型相比,其性能优越.
- 优化的FCNN有效地检测和分类乳腺癌亚型.
结论:
- 改进的模型,包括双边过,MFCM,MCSO和FCNN,为乳腺癌检测和分类提供了显著的改进.
- 该研究成功地解决了先前方法的局限性,提供了更强大,更准确的诊断工具.
- 这些发现表明,人工智能驱动的乳腺癌诊断有望走向.
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