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：基于深度学习的复合管道，用于对儿童髓母细胞瘤及其类别进行分类


儿童髓母细胞瘤（MB）是一种威胁全球儿童的恶性肿瘤。它是导致死亡的最常见的儿童脑肿瘤。儿童MB及其类别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、降低死亡率具有重要意义。目前诊断 MB 的黄金标准是活检样本的组织病理学。然而，手动分析组织病理学图像复杂、昂贵、耗时，并且高度依赖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。本研究旨在引入一种名为 CoMB-Deep 的可靠计算机辅助流程，以根据组织病理学图像自动对 MB 及其类别进行高精度分类。本研究的主要挑战是缺乏儿童MB数据集，特别是其四个类别，且相关研究不充分。所有相关工作均基于深度学习（DL）或纹理分析特征提取。此类研究采用不同的特征来完成分类。此外，大多数只提取空间特征。尽管如此，CoMB-Deep 融合了纹理分析特征提取技术和深度学习方法的优点。 CoMB-Deep 由深度学习技术的组合组成。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，也能够减少融合特征的维度。接下来，CoMB-Deep 探索融合特征的最佳组合，从而使用两种搜索策略增强分类过程的性能。 然后，它在融合特征集 s 上采用两种特征选择技术。分类阶段使用双向 LSTM 网络。 CoMB-Deep的结果证明它是可靠的。结果还表明，与单个深度特征相比，使用两种搜索策略选择的特征集增强了 LSTM 的性能。 CoMB-Deep 与相关研究进行比较以验证其竞争力，并且这种比较证实了其​​稳健性和卓越性能。因此，CoMB-Deep可以帮助病理学家进行准确的诊断，降低手工诊断可能出现的误诊风险，加快分类过程，降低诊断成本。