混合深度神经网络与基于PCA的功能优化,以增强大脑瘤分类的优化
Binay Kumar Pandey1, Digvijay Pandey2, Tsair-Fwu Lee3
1Department of Information Technology, College of Technology, Govind Ballabh Pant University of Agriculture and Technology Pantnagar, Pantnagar, Uttarakhand, India. binaydece@gmail.com.
Scientific reports
|February 19, 2026
概括
一个新的混合PCA DenseNet121模型准确地分类脑瘤 (瘤,脑膜瘤,垂体,没有瘤) 准确率为95.89%. 这种方法将深度学习与纹理分析相结合,以提高诊断可靠性.
科学领域:
- 医学成像和人工智能 医学成像和人工智能
- 计算病理学计算病理学
- 机器学习在瘤学中的应用
背景情况:
- 由于不可预测的生长和复杂的医疗需求,大脑瘤存在诊断挑战.
- 准确地分类瘤类型对于有效的治疗计划至关重要.
研究的目的:
- 开发和评估混合主成分分析 (PCA) DenseNet121卷积神经网络,以改进脑瘤分类.
- 为了提高四种类型的分类准确性:质瘤,脑膜瘤,垂体瘤和没有瘤.
主要方法:
- 一个混合模型,将DenseNet121的深度特征与传统的纹理描述器相结合:灰色水平共发生矩阵 (GLCM),局部三元模式 (LTP) 和颜色连贯向量 (CCV).
- 使用CCV预处理MRI数据,使用27个离散强度容器来捕捉空间连接和微妙的强度关系.
- 使用主要组件分析 (PCA) 减少特征空间的维度,仅适用于训练数据,以防止偏差.
主要成果:
- 在大脑瘤类型中获得了95.89%的分类准确率.
- 精度,回忆和F1得分始终保持在94%以上.
- 通过一致的培训和验证准确度/损失曲线以及成功使用掉队正常化来有效减少过度装配.
结论:
- 整合多模式纹理描述器与深度特征提供了全面的瘤表示,最大限度地减少错误分类.
- 拟议的混合模型确保稳定的学习模式和可靠的诊断性能跨不同的临床数据集.
- 该方法有效地解决了脑瘤分类的挑战,为医学诊断提供了有前途的工具.
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