The application of deep transfer learning techniques has been successful in developing accurate systems for brain tumour classification on large-scale medical image databases. For small databases, feature learning by deep neural networks is not robust. The systems based on domain-specific hand-crafted features have limited accuracy. In this paper, the authors focus on developing accurate models that could be trained effectively using a smaller number of data samples. A siamese neural network (SNN) is designed to extract features from brain magnetic resonance imaging (MRI) images. The SNN is realised using a 3-layer, fully connected neural network. The designed SNN has lesser complexity and fewer parameters than deep transfer-learned convolutional neural networks (CNN). A nearest neighbourhood analysis, using Euclidean and Mahalanobis distances, is conducted on the SNN encoded feature space. The encoded feature space is two dimensional, such that the neighbourhood analysis is computationally less intensive. For the neighbourhood analysis, a k-nearest neighbour (k-NN) model is utilised. The proposed method is evaluated using three publicly available datasets, namely, Radiopaedia, Harvard and Figshare repositories. The respective classification accuracy on cross-validation is 92.6%, 98.5% and 92.6%. Other metrics used for the performance evaluation include F-score, Specificity and balanced accuracy. The underlying network architecture and the design choice of network layers allow the implementation of the SNN in environments with low computational resources. The SNN features are found to be more effective than the hand-designed features, and the deep transfer learned features for the stated problem.

中文翻译：

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嵌入特征空间中使用孪生神经网络和邻域分析的脑肿瘤分类

深度迁移学习技术的应用已经成功地在大规模医学图像数据库上开发了用于脑肿瘤分类的准确系统。对于小型数据库，深度神经网络的特征学习并不稳健。基于特定领域手工制作特征的系统精度有限。在本文中，作者专注于开发可以使用较少数量的数据样本进行有效训练的准确模型。孪生神经网络 (SNN) 旨在从脑磁共振成像 (MRI) 图像中提取特征。SNN 是使用 3 层全连接神经网络实现的。与深度迁移学习的卷积神经网络 (CNN) 相比，设计的 SNN 具有更低的复杂性和更少的参数。最近邻域分析，使用欧几里得距离和马哈拉诺比斯距离，在 SNN 编码的特征空间上进行。编码的特征空间是二维的，因此邻域分析的计算强度较低。对于邻域分析，使用了 k-最近邻 (k-NN) 模型。所提出的方法使用三个公开可用的数据集进行评估，即 Radiopaedia、Harvard 和 Figshare 存储库。交叉验证的分类准确率分别为 92.6%、98.5% 和 92.6%。用于性能评估的其他指标包括 F 分数、特异性和平衡准确度。底层网络架构和网络层的设计选择允许在低计算资源的环境中实现 SNN。发现 SNN 特征比手工设计的特征更有效，