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Updated: Jul 25, 2026

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交叉视觉变压器带有增强的生长优化器,用于在IoMT环境中检测乳腺癌

Mohamed Abd Elaziz¹, Abdelghani Dahou², Ahmad O Aseeri³

¹Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt; Faculty of Computer Science and Engineering, Galala University, Suze 435611, Egypt; Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates; MEU Research Unit, Middle East University, Amman 11831, Jordan.

Computational biology and chemistry

|May 30, 2024

概括

这项研究引入了一种新的AI方法来检测乳腺癌,使用CrossViT深度学习模型和MGO算法来选择特征. 这种方法提高了诊断的准确性,潜在地提高了患者的存活率和医疗保健效率.

科学领域:

医疗保健中的人工智能
医学成像分析医学成像分析
深度学习用于诊断.

背景情况:

乳腺癌仍然是妇女死亡的主要原因,需要准确和早期检测方法.
医疗物联网 (IoMT) 为先进的诊断工具提供了机会.
深度学习模型在提高癌症诊断的准确性方面显示出显著的希望.

研究的目的:

通过CrossViT和MGO算法提出一种用于乳腺癌检测的新技术.
通过先进的人工智能提高乳腺癌诊断的准确性和效率.
开发基于物联网的智能解决方案,用于临床决策支持.

主要方法:

利用了CrossViT深度学习模型,这是CNN和变压器的混合体.
采用了增强的增长优化器 (MGO) 算法来进行最佳的功能选择.
评估了三个公共乳腺癌数据集的方法:INbreast,MIAS和MiniDDSM.

主要成果:

开发的方法实现了与最先进的方法相比具有竞争力的性能.
该MGO算法提高了精度1.59% (INbreast),5.00% (MIAS) 和0.79% (MiniDDSM) 的精度.
CrossViT和MGO的组合有效地确定了乳腺癌检测的信息特征.

结论:

CrossViT和MGO组合为准确检测乳腺癌提供了一个强大的工具.
这项技术可以帮助临床医生进行诊断,从而有可能改善患者的治疗结果.
这种方法可以作为可部署的物联网解决方案来提高医疗保健服务质量 (QoS).

关键词:

乳腺癌乳腺癌乳腺癌深度学习是一种深度学习.选择功能选择功能选择.增长优化器增长优化器医疗事物的互联网医疗事物的互联网

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