A Reconfigurable CNN-2D Hardware Architecture for Real-Time Brain Cancer Multi-Classification on FPGA

Brain cancer classification using deep learning has gained significant attention due to its potential to improve early diagnosis and treatment planning. In this work, we propose a reconfigurable and hardware-optimized CNN-2D architecture implemented on FPGA for multiclass classification of brain tum...

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Principais autores:	Ayoub Mhaouch, Wafa Gtifa, Ibtihel Nouira, Abdessalem Ben Abdelali, Mohsen Machhout
Formato:	Artigo
Idioma:	Inglês
Publicado em:	MDPI AG 2026-02-01
coleção:	Algorithms
Assuntos:	brain cancer hardware acceleration artificial intelligence hardware implementation reconfigurable hardware architecture convolutional neural network
Acesso em linha:	https://www.mdpi.com/1999-4893/19/2/107
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