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使用像素分布和空间依赖的脑瘤分类,通过可解释的ML模型进行更高阶的统计测量.

Sharmin Akter1, Md Simul Hasan Talukder2, Sohag Kumar Mondal3

  • 1Biomedical Engineering, Jashore University of Science and Technology, Jashore, Bangladesh. sharmintalukder120@gmail.com.

Scientific reports
|October 29, 2024
PubMed
概括

这项研究引入了一种新的机器学习方法,用于从MRI扫描中准确地分类脑瘤. 该方法实现了卓越的诊断性能,为早期检测和治疗规划提供了可靠的工具.

关键词:
这就是为什么CBCBCBCB.DT DT DT DT DT DT DT DT DT DT DTD DTD DTD DTD DTD DTD DTD DTJ DTJ DTJ DTJ DTJ DTJ DTJ DTJ DTJ DTJ DTJ DTJ DTJ DTJ DTJ这就是为什么DWT DWT DWT额外的特点Classifier 分类器在GBGBGBGBGBGBGBGBGBGBGBGBGBGBGBGBGBGBGBGBGBGBGB在KNNImputer中使用.在 LGBMM 中.在这里,我们可以看到LRLRLRLR.这就是为什么MRI是MRI.在PCA中,PCA是PCA.这就是为什么RF是RF,RF是RF在SVM中,SVM是SVM.在XAI,XAI就是XAI.

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科学领域:

  • 医学成像分析 医学成像分析
  • 医疗保健中的机器学习
  • 计算神经科学是一种神经科学.

背景情况:

  • 准确的脑瘤诊断对于有效的治疗和改善患者结果至关重要.
  • 手动分析磁共振成像 (MRI) 数据是耗时且具有挑战性的.
  • 需要自动化,可靠的机器学习 (ML) 方法来检测脑瘤.

研究的目的:

  • 提出一种新的,全面的ML方法来识别和分类异常的大脑MRI图像.
  • 准确诊断三个常见的大脑瘤:质瘤,脑膜瘤和垂体瘤.
  • 通过使用先进的ML技术,提高脑瘤检测的诊断准确性和效率.

主要方法:

  • 使用第一顺序,第二顺序和离散波纹变换 (DWT) 统计数据进行特征提取.
  • 使用KNNImputer处理缺失的数据,并使用ExtratreesClassifier和PCA进行特征选择/尺寸缩小.
  • 培训和评估七个ML模型 (RF,GB,CB,SVM,LGBM,DT,LR) 的k倍交叉验证.
  • 利用可解释的人工智能 (XAI) 进行透明的模型评估和洞察.

主要成果:

  • 提出的综合方法实现了最高的准确性,精度,回忆,F1得分,MCC,卡帕,AUC-ROC和R2.
  • 该方法在七个评估的机器学习模型中显示了最低的损失.
  • 该模型的有效性在FigshareMRI脑图像数据集上得到了证明.

结论:

  • 开发的ML方法提供了一种高效和准确的方法,用于从MRI数据中对脑瘤进行分类.
  • 该研究强调了先进的特征提取,选择和ML模型在医学诊断中的潜力.
  • 这些发现支持了这种方法在使用公开可用的数据集的各种分析任务中的适用性.