Brain tumor segmentation is an important content in medical image processing, and it is also a very common research in medicine. Due to the development of modern technology, it is very valuable to use deep learning (DL) and multimodal MRI images to study brain tumor segmentation. In order to solve the problems of low efficiency and low accuracy of brain tumor segmentation, this paper proposes DL to conduct research on multimodal MRI image segmentation, aiming to make accurate diagnosis and treatment for doctors. In addition, this paper constructs an automatic diagnosis system for brain tumors, uses GLCM and discrete wavelet transform (DWT) to extract features from MRI images, and then uses a convolutional neural network (CNN) for final diagnosis; finally, through four. The comparison of the results between the two algorithms proves that the CNN algorithm has the better processing power and higher efficiency.

中文翻译：

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基于深度神经网络和多模态 MRI 图像的脑肿瘤 3D 自动分割

脑肿瘤分割是医学图像处理中的重要内容，也是医学中非常常见的研究。由于现代技术的发展，利用深度学习（DL）和多模态MRI图像来研究脑肿瘤分割非常有价值。为了解决脑肿瘤分割效率低、精度低的问题，本文提出DL进行多模态MRI图像分割研究，旨在为医生做出准确的诊断和治疗。此外，本文构建了脑肿瘤自动诊断系统，使用GLCM和离散小波变换（DWT）从MRI图像中提取特征，然后使用卷积神经网络（CNN）进行最终诊断；终于，通过了四。两种算法的结果对比证明CNN算法具有更好的处理能力和更高的效率。