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基于计算机视觉的多个脑瘤的高效细分和分类,使用计算机断层扫描图像

Aqib Ali¹, Xinde Li^2,3, Wali Khan Mashwani⁴

¹Key Laboratory of Measurement and Control of CSE, School of Automation, Southeast University, Nanjing, 210096, China.

Scientific reports

|September 1, 2025

概括

计算机视觉可以有效地从CT扫描中分类六种脑瘤. 多层感知器的准确率达到了97.83%,证明了这些技术在神经瘤诊断中的潜力.

科学领域:

医学成像
人工智能
神经瘤学

背景情况:

大脑瘤是一个重大的诊断挑战.
准确的瘤类型分类对于有效的治疗计划至关重要.
计算机断层扫描 (CT) 是脑瘤检测的主要成像方法.

研究的目的:

通过CT扫描来评估计算机视觉 (CV) 技术对六种类型的脑瘤 (良性和恶性) 的有效性.
开发和验证用于自动化脑瘤分类的强大框架.
为了比较不同CV分类器对此任务的性能.

主要方法:

预处理了900个CT扫描数据集,包括使用自动化二进制值基于模糊c-means细分 (ABTFCS) 的降噪和感兴趣区域 (ROI) 提取.
从每个ROI中提取了135个统计多特征,并使用基于相关性的特征选择选择了12个特征的优化集合.
使用十倍交叉验证评估了五个CV分类器 (MLP,BayesNet,PART,随机树,随机选分类器).

主要成果:

预处理和特征提取管道产生了精细的分类数据集.
特征选择确定了区分瘤类型的最相关的统计属性.
在超参数调整后,多层感知器 (MLP) 实现了最高的分类准确率97.83%.

结论:

计算机视觉技术,特别是MLP,在CT扫描中对大脑瘤进行分类方面表现出很高的有效性.
提出的框架为脑瘤诊断提供了一个有前途的自动化方法.
进一步的研究可以探索更大的数据集和先进的CV模型以提高诊断准确性.

关键词:

在ABTFCS 大脑瘤计算机视觉多层感知子优化统计多功能

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