Department of Computer Science and Engineering, University of Bologna, Cesena, FC, Italy.
Institute of Mathematics and Computer Science (ICMC), University of São Paulo (USP), São Carlos, SP, Brazil.
Adv Neurobiol. 2024;36:557-570. doi: 10.1007/978-3-031-47606-8_29.
Brain tumor detection is crucial for clinical diagnosis and efficient therapy. In this work, we propose a hybrid approach for brain tumor classification based on both fractal geometry features and deep learning. In our proposed framework, we adopt the concept of fractal geometry to generate a "percolation" image with the aim of highlighting important spatial properties in brain images. Then both the original and the percolation images are provided as input to a convolutional neural network to detect the tumor. Extensive experiments, carried out on a well-known benchmark dataset, indicate that using percolation images can help the system perform better.
脑肿瘤检测对于临床诊断和有效治疗至关重要。在这项工作中，我们提出了一种基于分形几何特征和深度学习的脑肿瘤分类的混合方法。在我们提出的框架中，我们采用分形几何的概念来生成一个“渗滤”图像，旨在突出脑图像中的重要空间属性。然后，将原始图像和渗滤图像都作为输入提供给卷积神经网络，以检测肿瘤。在一个著名的基准数据集上进行的广泛实验表明，使用渗滤图像可以帮助系统更好地执行。
