A brain tumor is an abnormal growth of cells inside the skull. Malignant brain tumors are among the most dreadful types of cancer with direct consequences such as cognitive decline and poor quality of life. Analyzing magnetic resonance imaging (MRI) is a popular technique for brain tumor detection. In this paper, we use these images to train our new hybrid paradigm which consists of a neural autoregressive distribution estimation (NADE) and a convolutional neural network (CNN). We subsequently test this model with 3064 T1-weighted contrast-enhanced images with three types of brain tumors. The results demonstrate that the hybrid CNN-NADE has a high classification performance as regards the availability of medical images are limited.

脑肿瘤是颅骨内部细胞的异常生长。恶性脑肿瘤是最可怕的癌症之一，其直接后果是认知能力下降和生活质量下降。分析磁共振成像（MRI）是一种用于脑肿瘤检测的流行技术。在本文中，我们使用这些图像来训练新的混合范例，该范例由神经自回归分布估计（NADE）和卷积神经网络（CNN）组成。随后，我们使用3064种T1加权对比增强图像和三种类型的脑瘤对该模型进行了测试。结果表明，就医学图像的可用性而言，混合CNN-NADE具有较高的分类性能。