乳腺癌检测采用堆叠组合模型,具有卷积特征
Hanen Karamti1, Raed Alharthi2, Muhammad Umer3
1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Cancer biomarkers : section A of Disease markers
|December 31, 2023
概括
这项研究引入了一种先进的组合模型,用于精确检测乳腺癌. 结合卷积神经网络 (CNN) 与随机森林和支持矢量分类器,它实现了99.99%的准确性,提高了早期诊断和生存率.
科学领域:
- 医学成像和诊断 医学成像和诊断
- 医疗保健中的机器学习
- 在瘤学瘤学.
背景情况:
- 乳腺癌仍然是妇女死亡的主要原因,特别是在发展中国家.
- 早期和准确的诊断对于有效的治疗和改善患者存活率至关重要.
- 当前的自动诊断方法虽然有希望,但往往缺乏所需的准确性.
研究的目的:
- 开发一个高度准确的组合模型,用于自动检测乳腺癌.
- 通过将优化特征提取与机器学习分类器集成来提高诊断准确性.
- 改进现有的最先进的乳腺癌诊断模型.
主要方法:
- 开发了一个组合模型,结合了随机森林和支持向量分类器.
- 使用优化的卷积神经网络 (CNN) 进行了自动特征提取.
- 该模型的性能使用威斯康星数据集进行了评估,比较了原始和基于CNN的特征.
主要成果:
- 基于CNN的特征在乳腺癌检测方面显著超过了原始特征.
- 拟议的整体模型实现了99.99%的特殊精度.
- 对比分析表明,拟议模型的性能优于现有方法.
结论:
- 拟议的整体模型为自动乳腺癌检测提供了一个高度准确的解决方案.
- 通过CNN优化功能提取是实现卓越诊断性能的关键.
- 这种方法具有显著的潜力,可以改善早期检测和乳腺癌护理中的患者结果.
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