Ayoub Mhaouch1,*, Wafa Gtifa2, Turke Althobaiti3, Hamzah Faraj4, Mohsen Machhout1
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5637-5663, 2025, DOI:10.32604/cmc.2025.065525
- 30 July 2025
Abstract In medical imaging, accurate brain tumor classification in medical imaging requires real-time processing and efficient computation, making hardware acceleration essential. Field Programmable Gate Arrays (FPGAs) offer parallelism and reconfigurability, making them well-suited for such tasks. In this study, we propose a hardware-accelerated Convolutional Neural Network (CNN) for brain cancer classification, implemented on the PYNQ-Z2 FPGA. Our approach optimizes the first Conv2D layer using different numerical representations: 8-bit fixed-point (INT8), 16-bit fixed-point (FP16), and 32-bit fixed-point (FP32), while the remaining layers run on an ARM Cortex-A9 processor. Experimental results demonstrate that FPGA acceleration significantly outperforms the… More >
Login
Register
