Brain tumors are among the most serious cancers that can have a negative impact on a person’s quality of life. The magnetic resonance imaging (MRI) analysis detects abnormal cell growth in the skull. Recently, machine learning models such as artificial neural networks have been used to detect brain tumors more quickly. To classify brain tumors, this research introduces the Y-net, a new convolutional neural network (CNN) based on the convolutional U-net architecture. We apply a NADE concatenation method in pre-processing the MR images for enhanced Y-net performance. We put our approach to the test using two MRI datasets of brain tumors. The first dataset contains three different types of brain tumors, while the second dataset includes a separate category for healthy brains. We show that our model is resistant to white noise and can obtain excellent classification accuracy with a limited number of medical images.

脑肿瘤是最严重的癌症之一，会对人的生活质量产生负面影响。磁共振成像 (MRI) 分析检测颅骨中的异常细胞生长。最近，人工神经网络等机器学习模型已被用于更快地检测脑肿瘤。为了对脑肿瘤进行分类，本研究引入了 Y-net，这是一种基于卷积 U-net 架构的新型卷积神经网络 (CNN)。我们应用 NADE 连接方法对 MR 图像进行预处理以增强 Y-net 性能。我们使用两个脑肿瘤 MRI 数据集对我们的方法进行了测试。第一个数据集包含三种不同类型的脑肿瘤，而第二个数据集包含一个单独的健康大脑类别。