Glioblastoma (GBM) is a stage 4 malignant tumor in which a large portion of tumor cells are reproducing and dividing at any moment. These tumors are life threatening and may result in partial or complete mental and physical disability. In this study, we have proposed a classification model using hybrid deep belief networks (DBN) to classify magnetic resonance imaging (MRI) for GBM tumor. DBN is composed of stacked restricted Boltzmann machines (RBM). DBN often requires a large number of hidden layers that consists of large number of neurons to learn the best features from the raw image data. Hence, computational and space complexity is high and requires a lot of training time. The proposed approach combines DTW with DBN to improve the efficiency of existing DBN model. The results are validated using several statistical parameters. Statistical validation verifies that the combination of DTW and DBN outperformed the other classifiers in terms of training time, space complexity and classification accuracy.

中文翻译：

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使用混合深度信念网络分析MRI扫描以检测胶质母细胞瘤肿瘤

胶质母细胞瘤（GBM）是4期恶性肿瘤，其中大部分肿瘤细胞随时繁殖并分裂。这些肿瘤危及生命，并可能导致部分或完全的精神和身体残疾。在这项研究中，我们提出了一种使用混合深度信念网络（DBN）对GBM肿瘤进行磁共振成像（MRI）进行分类的分类模型。DBN由堆叠式受限玻尔兹曼机（RBM）组成。DBN通常需要包含大量神经元的大量隐藏层，以从原始图像数据中学习最佳功能。因此，计算和空间复杂度很高，并且需要大量的训练时间。所提出的方法将DTW与DBN相结合，以提高现有DBN模型的效率。使用几个统计参数验证结果。