This paper simply presents a state of the art survey among the machine learning based approaches for the Glioma classification. As Glioma classification is a very challenging task in the field of medical science and this task is well addressed and taken by the fraternity of machine learning experts, who are working day and night to devise automated approaches that can automate this whole process of Glioma accurate classification from the various medical imaging modalities like Magnetic resonance imaging (MRI), Computed tomography (CT) etc. Although present machine learning techniques offers an opportunity to come up with a highly accurate and automated Glioma classification approach, by performing fusion among the various medical imaging modalities as well as utilizing the various features derived from the multi-modality medical imaging data. This paper also proposed an efficient and accurate automated approach of Glioma classification for the comparison analysis. This proposed approach is based on the use of hybrid ensemble learning model and hybrid feature extraction method, which relies on the Discrete wavelet Decomposition (DWD), Central pixel Neighbourhood Binary pattern (CNBP) and GLRLM (Gray level run length Matrix) methods in order to classify the Glioma (type of mostly diagnosed brain tumors) into Low grade Glioma and High grade Glioma from the fused MRI sequences. Improved eXtreme Gradient Boosting classifier is the hybrid ensemble learning model, which is used in this paper for the first time along with the proposed hybrid texture feature extraction method. Further this proposed approach is compared with the already existing state of the art approaches, which are based on the various machine learning classifiers like Support vector machine (SVM), K-Nearest neighbor (KNN), Naïve Bayes (NB) etc. and conventional feature extraction methods in order to present a comprehensive and practical comparison study. The proposed approach is evaluated on the balanced large size local dataset consisting of MRI images of low and high grade Glioma collected from the various MRI centers located in Madhya Pradesh, India as well as on the popular global datasets like, BRATS 2013 and BRATS 2015 with various MRI fusion combinations (T1 + T1C + T2 + Flair, T1 + T1C + T2, T1 + T1C + Flair, T1C + T2 + Flair etc.). The proposed approach employing Improved eXtreme Gradient Boosting ensemble model offers highest accuracy of above 90% on the local dataset with the fusion of T1C + T2 + Flair MRI sequences.

本文仅介绍了基于机器学习的神经胶质瘤分类方法中的最新技术调查。由于胶质瘤分类在医学领域是一项非常具有挑战性的任务，机器学习专家的兄弟们很好地解决了这一任务，他们日夜工作，以设计出可以自动完成胶质瘤准确分类整个过程的自动化方法尽管目前的机器学习技术提供了通过在各种医学成像之间进行融合来提供高度准确和自动化的神经胶质瘤分类方法的机会，但它来自各种医学成像模式模态以及利用从多模态医学成像数据得出的各种特征。本文还提出了一种高效，准确的胶质瘤分类自动化方法，用于比较分析。该方法基于混合集成学习模型和混合特征提取方法的使用，该方法依序依赖离散小波分解（DWD），中心像素邻域二进制模式（CNBP）和GLRLM（灰度级运行长度矩阵）方法从融合的MRI序列中将脑胶质瘤（诊断为脑肿瘤的类型）分为低度脑胶质瘤和高度脑胶质瘤。改进的eXtreme Gradient Boosting分类器是一种混合集成学习模型，本文首次与提出的混合纹理特征提取方法一起使用。此外，将该提议的方法与现有的现有方法进行了比较，它们基于各种机器学习分类器（例如支持向量机（SVM），K最近邻（KNN），朴素贝叶斯（NB）等）和常规特征提取方法，以进行全面而实用的比较研究。在平衡的大型局部数据集（包括从印度中央邦的各个MRI中心收集的低级和高级胶质瘤的MRI图像）以及流行的全球数据集（如BRATS 2013和BRATS 2015）上对所提出的方法进行了评估。各种MRI融合组合（T1 + T1C + T2 + Flair，T1 + T1C + T2，T1 + T1C + Flair，T1C + T2 + Flair等）。提出的方法采用改进的eXtreme梯度增强集成模型，在T1C + T2 + Flair MRI序列融合的情况下，在本地数据集上可提供90％以上的最高准确性。