Childhood medulloblastoma is a case of a childhood brain tumour that requires close attention due to the low survival rate. Effective prognosis depends a lot on accurate detection of its subtype. The present study proposes a texture‐based computer‐aided categorization of childhood medulloblastoma samples. According to the World Health Organization, it has four subtypes (desmoplastic, classic, nodular and large). Classification is done in two levels: (i) normal and abnormal and (ii) its four subtypes. The system is evaluated on indigenous patient samples collected from the region. The main objective of database generation is to create a data set of childhood medulloblastoma samples since there exists no available benchmark data set. The proposed framework for automated classification is based on the architectural property and the distribution of cells. Five texture features were extracted for the feature set, namely: grey‐level co‐occurrence matrix, grey‐level run length matrix, first‐order histogram features, local binary pattern and Tamura features. The performance of each feature set was evaluated, both individually and in combinations, using five different classifiers. Fivefold cross‐validation was used for training and testing the data set. Experiments on both individual feature sets and combinations (best‐2, best‐3, best‐4 and all‐5) of feature sets were evaluated based on the accuracy of performance. It was revealed that the combined best‐4 feature set resulted in the highest accuracy of 91.3%. The precision, recall and specificity were 0.913, 0.913 and 0.97, respectively. Significantly, it implied that the all‐5 feature set is not necessary to have a useful classification. Feature reduction by principal component analysis resulted in increased accuracy of 96.7%.

中文翻译：

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基于结构分析将儿童髓母细胞瘤分为 WHO 定义的多个亚型

儿童髓母细胞瘤是一种儿童脑肿瘤，由于存活率低，需要密切关注。有效的预后很大程度上取决于对其亚型的准确检测。本研究提出了一种基于纹理的计算机辅助儿童髓母细胞瘤样本分类方法。根据世界卫生组织，它有四种亚型（促纤维增生型、经典型、结节型和大型）。分类分为两个级别：（i）正常和异常以及（ii）其四个亚型。该系统是根据从该地区收集的土著患者样本进行评估的。数据库生成的主要目标是创建儿童髓母细胞瘤样本的数据集，因为不存在可用的基准数据集。建议的自动分类框架基于架构属性和单元格的分布。为特征集提取了五个纹理特征，即：灰度共生矩阵、灰度游程矩阵、一阶直方图特征、局部二值模式和田村特征。使用五个不同的分类器单独和组合评估每个特征集的性能。五重交叉验证用于训练和测试数据集。基于性能的准确性对单个特征集和特征集组合（最佳 2、最佳 3、最佳 4 和所有 5）的实验进行了评估。结果表明，组合的 best-4 特征集的准确率最高，为 91.3%。准确率、召回率和特异性分别为 0.913、0.913 和 0.97，分别。重要的是，它暗示所有 5 特征集对于有用的分类不是必需的。通过主成分分析进行的特征减少使准确度提高了 96.7%。