: 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.File | Dimensione | Formato | |
---|---|---|---|
Fractals_Brain.pdf
Open Access dal 13/10/2024
Tipo:
Postprint
Licenza:
Licenza per accesso libero gratuito
Dimensione
1.74 MB
Formato
Adobe PDF
|
1.74 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.