Visual evaluation of many magnetic resonance images is a difficult task. Therefore, computer-assisted brain tumor classification techniques have been proposed. These techniques have several drawbacks or limitations. Capsule based neural networks are new approaches that can preserve spatial relationships of learned features using dynamic routing algorithm. By this way, not only performance of tumor recognition increases but also sampling efficiency and generalisation capability improves. Therefore, in this work, a Capsule Network (CapsNet) is used to achieve fully automated classification of tumors from brain magnetic resonance images. In this work, prevalent three types of tumors (pituitary, glioma and meningioma) have been handled. The main contributions in this paper are as follows: 1) A comprehensive review on CapsNet based methods is presented. 2) A new CapsNet topology is designed by using a Sobolev gradient-based optimisation, expectation-maximisation based dynamic routing and tumor boundary information. 3) The network topology is applied to categorise three types of brain tumors. 4) Comparative evaluations of the results obtained by other methods are performed. According to the experimental results, the proposed CapsNet based technique can achieve extraction of desired features from image data sets and provides tumor classification automatically with 92.65% accuracy.

中文翻译：

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CapsNet拓扑从大脑图像和比较评估中对肿瘤进行分类

对许多磁共振图像进行视觉评估是一项艰巨的任务。因此，已经提出了计算机辅助的脑肿瘤分类技术。这些技术具有几个缺点或局限性。基于胶囊的神经网络是可以使用动态路由算法保留学习特征的空间关系的新方法。通过这种方式，不仅提高了肿瘤识别的性能，而且还提高了采样效率和泛化能力。因此，在这项工作中，使用胶囊网络（CapsNet）从大脑磁共振图像中实现肿瘤的全自动分类。在这项工作中，已经处理了三种常见的肿瘤（垂体，神经胶质瘤和脑膜瘤）。本文的主要贡献如下：1）对基于CapsNet的方法进行了全面的综述。2）通过使用基于Sobolev梯度的优化，基于期望最大化的动态路由和肿瘤边界信息来设计新的CapsNet拓扑。3）网络拓扑被应用于对三种类型的脑肿瘤进行分类。4）对通过其他方法获得的结果进行比较评估。根据实验结果，提出的基于CapsNet的技术可以实现从图像数据集中提取所需特征，并以92.65％的精度自动提供肿瘤分类。4）对通过其他方法获得的结果进行比较评估。根据实验结果，提出的基于CapsNet的技术可以实现从图像数据集中提取所需特征，并以92.65％的精度自动提供肿瘤分类。4）对通过其他方法获得的结果进行比较评估。根据实验结果，提出的基于CapsNet的技术可以实现从图像数据集中提取所需特征，并以92.65％的精度自动提供肿瘤分类。