SSDA_AOA:用阿基米德的优化算法堆叠的稀疏否定自编码器,基于对组织病理图像的口腔癌检测
R Sathish Kumar1, M Govindarajan1
1Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu, India.
Oral diseases
|February 2, 2026
概括
这项研究引入了用于口腔癌检测的新型计算机辅助诊断框架,达到95.38%的准确性. 深度学习和优化方法提高了早期诊断,改善了患者的治疗结果.
科学领域:
- 在瘤学瘤学.
- 计算机科学 计算机科学
- 医疗成像医学成像
背景情况:
- 口腔癌诊断带来了重大挑战,影响了患者的治疗结果.
- 早期检测对于有效治疗和改善生存率至关重要.
- 目前的诊断工具需要改进,以提高准确性和效率.
研究的目的:
- 为口腔癌开发一种新的计算机辅助诊断 (CAD) 框架.
- 利用深度学习 (DL) 和优化技术来提高早期检测.
- 改善口腔癌病例的诊断准确性和患者的治疗结果.
主要方法:
- 用NASNet-Large模型进行特征提取,先处理并分析了组织病理图像.
- 阿基米德的优化算法 (AOA) 执行了同时的维度减小和超参数调整.
- 一个堆叠的稀疏排斥自编码器 (SSDA) 分类器被用于强大的隐性表示学习.
主要成果:
- 拟议的SSDA-AOA框架实现了高绩效指标.
- 准确率: 95.38% 的准确率.
- 精度:95.15%,灵敏度:91.78%,特异性:91.85%,F1得分:93.72%. 测试时间: 时间: 时间: 时间:
结论:
- 该SSDA-AOA框架显示了早期和精确的口腔癌检测的巨大潜力.
- 这种方法可以促进及时干预,从而改善患者的治疗结果.
- 这项研究强调了在瘤诊断中需要先进的计算工具.
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