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ShQDFHNet:使用MRI图像检测脑瘤的Shepard量子向前扩展波网

G V Sam Kumar¹, Rajesh Kumar T²

¹Research Scholar, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.

Archives of physiology and biochemistry

|August 17, 2025

概括

一个新的深度学习模型,谢帕德量子扩展前向波网络 (ShQDFHNet),通过MRI扫描来增强大脑瘤检测. 这种方法提高了准确性和特征学习,以获得更好的诊断结果.

科学领域:

医学成像和诊断医学成像和诊断
医疗保健中的人工智能
神经瘤学神经瘤学

背景情况:

大脑瘤是一个重大的健康挑战,目前的诊断系统主要依赖于医学成像.
准确和早期发现脑瘤对于有效的治疗计划和患者的治疗结果至关重要.

研究的目的:

开发和评估一种新的深度学习模型,即Shepard量子扩展前网络 (ShQDFHNet),用于使用MRI扫描进行增强的大脑瘤检测.
通过先进的图像处理和特征提取技术,提高脑瘤细分和检测的准确性和效率.

主要方法:

使用高提升过来强调关键特征的图像增强.
通过Log-Cosh点智能金字塔注意力网络 (Log-Cosh PPANet) 精确的瘤细分.
提取纹理特征,包括空间灰级依赖矩阵 (SGLDM) 和灰级共发生矩阵 (GLCM).
使用ShQDFHNet模型检测大脑瘤,集成谢帕德卷积神经网络 (ShCNN) 和量子扩展卷积神经网络 (QDCNN) 与前向波分析网络层.

主要成果:

ShQDFHNet模型在脑瘤MRI数据集上表现出高性能.
获得了90.69%的准确性,91.14%的真阳性率 (TPR) 和90.61%的真阴性率 (TNR),具有9倍的交叉验证.

结论:

集成高提升过,Log-Cosh PPANet和基于纹理的功能显著提高了输入数据质量,并使MRI扫描中精确的瘤细分成为可能.
拟议的ShQDFHNet模型有效地提高了特征学习,从而从MRI数据中检测大脑瘤的性能强.

关键词:

大脑瘤脑瘤是什么灰色级别的同时发生矩阵 (GLCM)谢帕德卷积神经网络 (ShCNN) 是一种神经网络.脑瘤检测发现脑瘤检测高提升过器高提升过器

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Magnetic Resonance Imaging

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