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一种混合M-DbneAlexnet用于使用MRI图像检测脑瘤

Jayasri Kotti¹, Vidyadhari Chalasani², Creesy Rajan³

¹Department of Information Technology, GMR Institute of Technology, Rajam, Andhra Pradesh, India.

Archives of physiology and biochemistry

|July 29, 2025

概括

这项研究引入了一种新的混合深度学习模型M-DbneAlexnet,用于准确地从MRI扫描中检测和细分脑瘤. 该模型实现了高精度,改善了早期诊断和患者存活率.

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 (BT) 是一个重大的健康挑战,早期检测对于改善患者存活率至关重要.
现有的BT检测方法经常受到高计算成本,有限的特征歧视和糟糕的泛化的影响.
磁共振成像 (MRI) 是可视化大脑结构和识别异常的一个关键模式.

研究的目的:

开发一种使用MRI进行脑瘤检测和细分的有效和高效方法.
解决现有的BT检测技术的局限性,包括计算复杂性和泛化问题.
通过先进的深度学习技术,提高脑瘤诊断的准确性和速度.

主要方法:

一个混合深度学习网络,MobileNet-深批量规范化的eLuAlexNet (M-DbneAlexnet),被开发用于BT检测和细分.
图像增强使用零碎线性转换 (PLT) 函数进行.
脑瘤区域使用变压器脑瘤细分 (TransBTSV2) 进行细分,然后通过M-DbneAlexnet模型进行特征提取和检测.

主要成果:

拟议的M-DbneAlexnet模型实现了高性能指标:92.68%的精度,93.02%的灵敏度和92.85%的特异性.
该模型在区分瘤和健康组织方面表现出有效性,即使使用有限的数据集.
与现有方法相比,该方法显示提高了培训速度.

结论:

M-DbneAlexnet模型提供了一个有前途的解决方案,用于准确和高效的脑瘤检测和MRI细分.
开发的方法在实现早期诊断方面具有实际实用性,可能降低脑瘤死亡率.
该模型的性能突显了混合深度学习架构在瘤医学图像分析中的潜力.

关键词:

在DbneAlexnet上使用.移动网络 (MobileNet) 是一个移动网络.移动网络-DbneAlexNet 移动网络-DbneAlexNet 移动网络在TransBTSV2中一块一块的线性转换.

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Magnetic Resonance Imaging

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