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基于深度学习信息融合和牛顿拉普森控制的海洋掠食者算法的无线囊内镜图像的胃肠道疾病分类

Saddaf Rubab¹, Muhammad Jamshed², Muhammad Attique Khan²

¹Department of Computer Engineering, College of Computing and Informatics, University of Sharjah, Sharjah, 27272, United Arab Emirates. srubab@sharjah.ac.ae.

Scientific reports

|September 1, 2025

概括

这项研究引入了计算机辅助诊断 (CAD) 系统,用于早期检测胃肠道疾病. 这种新型深度学习方法显著提高了诊断准确度, 有助于及时识别癌症.

科学领域:

医学成像
人工智能
胃肠病学

背景情况:

癌症是全球死亡的主要原因.
通过内镜诊断胃肠道 (GIT) 异常面临挑战,因为观察者之间的变化和专业知识.
细微的病变可能会被忽视, 延迟癌症的早期诊断.

研究的目的:

开发一个先进的计算机辅助诊断 (CAD) 系统,用于早期发现和分类GIT疾病.
提高诊断准确度,克服手动内镜解释的局限性.
利用深度学习和特征优化来改善GIT异常的识别.

主要方法:

一个新的CAD系统结合了修改深度学习模型 (ResNet18,ResNet50与EFP层) 的功能融合和最佳功能选择.
使用Kvasir V1,Kvasir V2和Hyperkvasir数据集进行培训和验证.
通过上下过,热场传播 (EFP) 层和牛顿-拉普森海洋捕食者优化 (NRMPO) 来提高对比度.

主要成果:

拟议的CAD系统实现了高精度:Kvasir V1的99. 0%,Kvasir V2的89. 6%,Hyperkvasir的82. 7%.
使用新型平均值方法的特征融合提高了分类性能.
废除研究证实了中间步骤在达到报告准确性的有效性.

结论:

与最先进的技术相比,开发的CAD系统表现出卓越的性能.
这种方法为GIT疾病的诊断提供了更高的准确性和精度.
这种方法具有早期可靠的胃肠道异常检测的巨大潜力.

关键词:

进行分类深度学习合并公司优化情况胃癌无线囊内镜

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