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Updated: Jun 7, 2026

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混合碎形ConvXGBNet用于脑瘤分类与日志-cosh子损失 - 基于MRI图像的V-Net级联基于细分

R Carol Praveen1, P Kasthuri Rengen2, Divya Francis3

  • 1Associate Professor, Department of ECE, SSM Institute of Engineering and Technology, Dindigul, Tamilnadu, 624002, India. carolpraveenraja@gmail.com.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
|December 15, 2025
PubMed
概括

一个新的Fractal-ConvXGBNet模型准确地从MRI扫描中分类大脑瘤,达到90%以上的准确性. 这种人工智能驱动的方法可以提高早期发现和诊断脑瘤,如质瘤,脑膜瘤和垂体瘤.

关键词:
级联式V-Net连接方式在 ConvXGBGB 中使用.卷积式 eXtreme 的梯度增强.在FractalNet中,我们可以使用FractalNet.高提升过器的高提升过器.记录-Cosh子损失的损失.空间灰色级依赖矩阵

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