Percolation Images: Fractal Geometry Features for Brain Tumor Classification

• Autor(a) principal: Lumini, Alessandra
• Data de Publicação: 2024
• Outros Autores: Roberto, Guilherme Freire, Neves, Leandro Alves [UNESP], Martins, Alessandro Santana, do Nascimento, Marcelo Zanchetta
• Tipo de documento: Capítulo de livro
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1007/978-3-031-47606-8_29; https://hdl.handle.net/11449/308182
• topic: Brain tumors; Classification ensemble; Deep learning; Feature representations; Fractal features

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.