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 tumors from MRI images. The contribution of this study lies in the development of a lightweight CNN model and a modular hardware design, where three key IP coresConv2D, MaxPooling, and ReLUare architected with parameterizable kernels, efficient dataflow, and optimized memory reuse to support real-time processing on resource-constrained platforms. These IPs are iteratively reconfigured to process each CNN layer, enabling flexibility while maintaining low latency. To evaluate the proposed architecture, we first implement the model in software on a Dual-Core Cortex-A9 processor and then deploy the hardware-accelerated version on an XC7Z020 FPGA. Performance is assessed in terms of execution time, power consumption, and classification accuracy. The FPGA implementation achieves a 93.21% reduction in latency and a 67.5% reduction in power consumption, while maintaining a competitive accuracy of 96.09% compared with 98.43% for the software version. These results demonstrate that the proposed reconfigurable FPGA-based architecture offers a strong balance between accuracy, real-time performance, and energy efficiency, making it highly suitable for embedded brain tumor classification systems.