混合细分模型和基于CAViaR的Xception Maxout网络用于使用MRI图像检测脑瘤
Satha Swapna1, Yugandhar Garapati2
1Department of Computer Science and Engineering, School of Technology, GITAM Deemed to be University, Hyderabad, Telangana, India.
Medical & biological engineering & computing
|June 27, 2025
概括
这项研究介绍了Caviar_XM-Net,这是一种新的深度学习模型,用于使用MRI图像准确检测脑瘤 (BT). 鱼_XM-Net模型在识别脑瘤方面实现了高效率,改进了传统方法.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- 脑瘤 (BT) 检测对于及时治疗至关重要,但由于瘤的变异性而具有挑战性.
- 现有的细分和检测方法面临着瘤位置和大小变化的局限性.
- 磁共振成像 (MRI) 是可视化大脑结构和异常的一个关键模式.
研究的目的:
- 提出一种新的深度学习模型,Caviar_XM-Net,用于在MRI图像中增强大脑瘤检测.
- 为了解决由瘤大小和位置的变化引起的大脑瘤检测的复杂性.
- 通过综合方法提高脑瘤识别的准确性和效率.
主要方法:
- 使用自适应双边过器 (ABF) 进行MRI图像的预处理.
- 使用贝叶斯模糊集群 (BFC) 和多分支残留融合网络 (MRF-Net) 的瘤细分,通过RV系数结合输出.
- 使用局部最佳定向模式 (LOOP),卷积神经网络 (CNN),中间二进制模式 (MBP) 和局部Gabor XOR模式 (LGXP) 的特征提取.
- 使用拟议的有条件自回归风险值_Xception Maxout-Network (Caviar_XM-Net) 进行脑瘤检测.
主要成果:
- 鱼_XM-Net模型实现了高性能指标:91.59%的灵敏度,91.36%的准确性,90.83%的特异性,90.99%的精度,91.29%的F1-score.
- 与传统的大脑瘤检测技术相比,提出的方法显示出更高的性能.
- 有效地整合了图像消噪,细分,特征提取,以及一个新的深度学习网络.
结论:
- Caviar_XM-Net提供了一个高效和准确的解决方案,用于MRI中检测大脑瘤.
- 该研究强调了深度学习模型与医疗诊断的先进图像处理技术相结合的潜力.
- 开发的方法在神经瘤学和医学图像分析领域提供了有前途的进步.
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