FPGA-based brain tumor MRI classification using multi-scale discrete hahn moments and a lightweight CNN

Brain tumor classification from magnetic resonance imaging (MRI) is an important task in computer-aided diagnosis, requiring high accuracy to provide reliable clinical decision support and assist radiologists in their diagnostic workflow as a complementary tool, without aiming to replace expert clinical judgment. In this paper, we propose a hardware-oriented method for four-class brain tumor MRI classification, based on a multi-scale representation using discrete Hahn moments and a lightweight convolutional neural network. Instead of processing raw images directly, the proposed approach transforms each image into a compact multi-channel tensor obtained by extracting Hahn coefficients at three complementary spatial levels: global, regional, and local. This representation captures discriminative information at different scales while reducing input redundancy and enabling parallel execution on FPGA. The Hahn polynomial matrices are pre-computed offline, while multi-scale moment extraction and lightweight network inference are performed online on a Xilinx Zynq UltraScale+ ZCU106 platform. Furthermore, the student network is trained offline via knowledge distillation to improve accuracy under complexity constraints. Experiments conducted on the Kaggle Brain Tumor MRI dataset, comprising glioma , meningioma , no tumor and pituitary classes, show that the proposed method achieves an accuracy of 89.21% while significantly reducing model complexity compared to raw image-based approaches.