Childhood medulloblastoma (MB) is a threatening malignant tumor affecting children all over the globe. It is the foremost common pediatric brain tumor causing death. The early and accurate classification of childhood MB and its classes is of great importance to help doctors choose the suitable treatment and observation plan, avoid tumor progression, and lower death rates. The current gold standard for diagnosing MB is histopathology of biopsy samples. However, analyzing histopathological images manually is complicated, expensive, time-consuming, and highly dependent on pathologist expertise and skills which might cause inaccurate results in some cases. This study aims to introduce a reliable computer assistant pipeline called CoMB-Deep to automatically classify MB and its classes with high accuracy from histopathological images. The key challenge in this study is the lack of Childhood MB datasets especially its four classes and the inadequate related studies. All related works were based on either deep learning (DL) or textural analysis feature extractions. Such studies employed distinct features to accomplish classification. Besides, most of them only extracted spatial features. Nevertheless, CoMB-Deep blends the advantages of textural analysis feature extraction techniques and DL approaches. CoMB-Deep consists of a composite of DL techniques. Initially, it extracts spatial deep features from 10 CNNs. Then, it performs a feature fusion step using DWT which is a texture analysis method capable of reducing the dimension of fused features as well. Next, CoMB-Deep explores the best combination of fused features which enhances the performance of the classification process using two search strategies. Afterwards, it employs two feature selection techniques on the fused feature sets s. A bi-directional LSTM network is utilized for the classification phase. The results of CoMB-Deep prove that it is reliable. The results also indicate the feature sets selected using both search strategies have enhanced the performance of LSTM compared to individual deep features. CoMB-Deep is compared to related studies to verify its competitiveness and this comparison confirmed its robustness and outperformance. Hence, CoMB-Deep can assist pathologists in performing accurate diagnoses, reduce risks of misdiagnosis that could occur with manual diagnosis, accelerate the classification procedure, and decrease the cost of diagnosis.

中文翻译：

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CoMB-Deep：基于复合深度学习的管道，用于分类儿童髓母细胞瘤及其分类

儿童髓母细胞瘤（MB）是一种威胁性恶性肿瘤，影响着全球各地的儿童。它是引起死亡的最常见的小儿脑肿瘤。对儿童MB及其分类的早期准确分类对于帮助医生选择合适的治疗和观察计划，避免肿瘤进展和降低死亡率非常重要。当前诊断MB的金标准是活检样本的组织病理学。但是，手动分析组织病理学图像非常复杂，昂贵，耗时，并且高度依赖于病理学家的专业知识和技能，在某些情况下可能会导致结果不准确。这项研究的目的是引入一种可靠的计算机辅助管道，称为CoMB-Deep，以从组织病理学图像中自动高精度地对MB及其分类进行分类。这项研究的主要挑战是缺少儿童期MB数据集，尤其是其四个类别，以及相关研究不足。所有相关作品均基于深度学习（DL）或纹理分析特征提取。此类研究采用了独特的功能来完成分类。此外，它们中的大多数仅提取了空间特征。尽管如此，CoMB-Deep融合了纹理分析特征提取技术和DL方法的优势。CoMB-Deep由DL技术的组合组成。最初，它从10个CNN中提取空间深度特征。然后，它使用DWT执行特征融合步骤，DWT是一种纹理分析方法，也能够减小融合特征的尺寸。下一个，CoMB-Deep探索了融合功能的最佳组合，可使用两种搜索策略来增强分类过程的性能。之后，它在融合特征集s上采用了两种特征选择技术。双向LSTM网络用于分类阶段。CoMB-Deep的结果证明了它的可靠性。结果还表明，与单独的深层特征相比，使用两种搜索策略选择的特征集均增强了LSTM的性能。将CoMB-Deep与相关研究进行比较，以验证其竞争力，并且该比较证实了其​​坚固性和出色性能。因此，CoMB-Deep可以帮助病理学家进行准确的诊断，减少手动诊断可能导致的误诊风险，加快分类程序，