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通过合体学习和鱼优化算法改善宫癌诊断:CT和MRI在宫癌诊断中的比较研究

Eman Hussein Alshdaifat¹, Amer Mahmoud Sindiani², Salem Alhatamleh³

¹Department of Obstetrics and Gynecology, Faculty of Medicine, Yarmouk University, Irbid, Jordan.

Frontiers in oncology

|October 27, 2025

概括

本研究介绍了一种结合大视觉模型 (LVM) 和内部图像的AI模型,用于使用CT和MRI扫描精确检测子宫癌. 人工智能实现了高精度,改善了对良性和恶性瘤的诊断.

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:

宫癌是女性的主要恶性瘤.
计算机断层扫描 (CT) 和磁共振成像 (MRI) 是关键的诊断工具.
人工智能 (AI) 和计算机辅助诊断 (CAD) 在提高查方面表现有前途.

研究的目的:

为了比较CT和MRI对宫癌的诊断准确度.
引入一种新的人工智能方法,将大型视觉模型 (LVM) 和内部图像结合起来,以改进分类.
为了减少对良性和恶性宫瘤的错误分类.

主要方法:

一个混合的人工智能模型,集成空间特征的 LVM 和深度特征的 InternImage (基于 InceptionV3).
利用鱼优化算法 (SOA) 进行动态重量选择,以增强模型概括性.
在两个新的数据集 (KAUH-CCTD,KAUH-CCMD) 上评估模型,用于对良性,恶性和正常的宫图像进行分类.

主要成果:

人工智能模型在通过CT图像诊断子宫癌时达到98.49%的准确性.
该模型使用MRI图像证明了92.92%的宫癌诊断准确度.
该方法有效地区分了良性,恶性和正常病例.

结论:

拟议的AI模型,利用LVM和InternImage,通过CT和MRI在宫癌检测中提供了卓越的准确性.
CT和MRI对于评估瘤范围和指导治疗决策至关重要.
医学成像对于监测治疗反应和检测疾病复发至关重要.

关键词:

这是一个CT图像CT图像CT图像CT图像.磁共振成像磁共振成像磁共振成像磁共振成像宫癌:子宫癌是一种癌症.分类医学图像医学图像计算机辅助诊断是指计算机辅助的诊断.深度学习是一种深度学习.妇科瘤学妇科瘤学

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