: 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.
Percolation Images: Fractal Geometry Features for Brain Tumor Classification / Lumini, Alessandra; Roberto, Guilherme Freire; Neves, Leandro Alves; Martins, Alessandro Santana; do Nascimento, Marcelo Zanchetta. - STAMPA. - 36:(2024), pp. 557-570. [10.1007/978-3-031-47606-8_29]
Percolation Images: Fractal Geometry Features for Brain Tumor Classification
Lumini, Alessandra
Primo
;
2024
Abstract
: 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.File | Dimensione | Formato | |
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