骨癌细胞预测使用增强的深度学习算法与优化技术
Mohanthi Kakarla1, K Padma Raju2
1Department of Electronics and Communication Engineering, Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh, India.
Asian Pacific journal of cancer prevention : APJCP
|March 7, 2026
概括
一个新的CS-MHC ResNet模型显著提高了自动骨癌检测准确度. 这种深度学习方法增强了特征选择和分类,为早期诊断提供了更可靠的工具.
科学领域:
- 医学成像分析分析 医学成像分析
- 计算瘤学是一种计算瘤学.
- 机器学习在医疗保健中的应用
背景情况:
- 早期发现骨癌对于患者的生存至关重要.
- 传统的诊断方法耗时,需要专门的专业知识.
- 需要自动化系统来提高骨癌诊断的准确性和效率.
研究的目的:
- 开发一种机器学习 (ML) 驱动的工具,用于增强骨癌检测和分类.
- 通过将深度学习 (DL) 与优化算法集成来提高诊断准确性.
- 创建一个临床适用模型,用于早期骨癌诊断.
主要方法:
- 采用了混合方法,将ResNet与Cuckoo Search修改登 (CS-MHC) 优化相结合.
- 使用Cuckoo搜索优化 (CSO) 进行功能选择和超参数调整.
- 将CS-MHC ResNet模型与传统DL模型 (VGG-16,Inception,Xception) 进行比较.
主要成果:
- CS-MHC ResNet实现了更高的分类准确度 (超过90%),灵敏度 (约85%),精度 (超过88%) 和F-测量 (大约1. 86%) 的情况.
- 集成的CSO增强了特征选择,提高了骨癌分类的有效性.
- 超越了传统模型,证明了混合优化和DL方法的有效性.
结论:
- CS-MHC ResNet 模型在自动化骨癌检测方面取得了重大进展.
- 该模型显示了临床应用的高潜力,提供了一个更有效和可靠的诊断工具.
- 未来的工作将专注于使用更大的数据集进行验证,并探索更简单的模型设计以获得更广泛的适用性.
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