: 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.

Lumini, A., Roberto, G.F., Neves, L.A., Martins, A.S., do Nascimento, M.Z. (2024). Percolation Images: Fractal Geometry Features for Brain Tumor Classification. Switzerland : Springer [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.
2024
The Fractal Geometry of the Brain
557
570
Lumini, A., Roberto, G.F., Neves, L.A., Martins, A.S., do Nascimento, M.Z. (2024). Percolation Images: Fractal Geometry Features for Brain Tumor Classification. Switzerland : Springer [10.1007/978-3-031-47606-8_29].
Lumini, Alessandra; Roberto, Guilherme Freire; Neves, Leandro Alves; Martins, Alessandro Santana; do Nascimento, Marcelo Zanchetta
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/966228
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