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通过深度学习模型和变压器集成组合来增强脑瘤MRI分类

Nawal Benzorgat¹, Kewen Xia¹, Mustapha Noure Eddine Benzorgat¹

¹School of Electronics and Information Engineering, Hebei University of Technology, Tianjin, China.

PeerJ. Computer science

|December 9, 2024

概括

这项研究引入了一种混合深度学习模型,用于准确检测脑瘤. 这种新的方法结合了转移学习和变压器编码器,在多个数据集上实现了超过98%的准确性,以改善癌症诊断.

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:

脑瘤是全球癌症死亡的主要原因.
早期和准确的检测对于改善患者存活率至关重要.
手动分析MRI数据用于脑瘤识别是耗时且具有挑战性的.

研究的目的:

开发一种精确有效的深度学习方法来诊断脑瘤.
利用转移学习和变压器编码器机制来提高诊断准确度.
为了解决手动MRI分析在早期脑瘤检测中的局限性.

主要方法:

开发了一个混合深度学习模型,集成转移学习和变压器编码器.
使用三种预训练模型 (DenseNet201,GoogleNet,InceptionResNetV2) 进行特征提取.
变压器编码器包含基于移动窗口的自我注意机制和多层感知器.

主要成果:

混合模型在三个公共数据集中实现了高准确性:99.34% (成),99.16% (BT-大-2c) 和98.62% (BT-大-4c).
与现有技术相比,拟议的模型表现出优越的性能.
在不同样本数量,平面和对比度的数据集中观察到一致的结果.

结论:

混合深度学习模型为精确的脑瘤诊断提供了可靠的解决方案.
这种方法显著改善了当前用于早期瘤检测和分类的方法.
这些发现突显了先进人工智能的潜力,可以减少与癌症相关的死亡率.

关键词:

脑瘤检测脑瘤检测脑瘤检测深度学习是一种深度学习.组合学习学习组合学习分析MRI数据的数据分析.医学成像医学成像多层感知子 (MLP) 多层感知子 (MLP)预先训练有素的模型转移了基于窗口的自我注意力转移学习转移学习变压器编码器编码器

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