基于乳腺超声波的癌症检测使用深度学习网络选择和特征优化
Amad Zafar1, Jawad Tanveer2, Muhammad Umair Ali1
1Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea.
Bioengineering (Basel, Switzerland)
|July 29, 2023
概括
这项研究引入了使用乳腺超声波图像的乳腺癌诊断框架. 均衡优化器算法实现了96.79%的准确性,改善了早期乳腺癌检测.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 早期乳腺癌 (BC) 检测和病变特征对于患者的预后至关重要.
- 乳房超声波 (BU) 是BC诊断的关键放射学工具.
研究的目的:
- 开发和评估基于图像的BU框架,用于诊断女性乳腺癌.
- 优化深度功能选择和网络架构,以提高诊断准确度.
主要方法:
- 利用预训练的深度学习网络从BU图像中提取特征.
- 采用了十个基于包装的优化算法,包括平衡优化器 (EO),以选择最佳的深度功能.
- 实施了网络选择算法,以确定表现最佳的预训练网络.
- 使用支持矢量机 (SVM) 分类器对病变进行分类.
主要成果:
- 均衡优化器 (EO) 算法在所有预训练模型中表现出卓越的性能.
- 使用ResNet-50与562个特征向量实现了96.79%的最高分类准确度.
- 当与EO算法相结合时,Inception-ResNet-v2实现了第二高的准确率96.15%,达到第二高的准确率.
- 结果与现有的文献发现进行了比较.
结论:
- 拟议的BU基于图像的框架有效地帮助诊断乳腺癌.
- 在BC检测中,EO算法对最佳的深度特征选择非常有效.
- 这种方法为提高乳腺癌诊断的准确性和效率提供了一个有希望的工具.
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