Design of Lightweight Custom IP Core on FPGA to Discriminate Brain Tumors of MR Data - Chatterjee, Soumita; Pandit, Soumya; Das, Arpita (Devices for Integrated Circuit, 2025)
In response to growing demand for efficient medical image processing methods on FPGA, this work presents the design of a custom IP core for brain tumor classification using morphological features. This IP core uses the ZYNQ 7000 Processing System on PYNQ Z2 platform. The present work focuses on minimization of classifier's t raining complexity by extracting 17 significant morphological features from tumor data. The tumors are then categorised with the best accuracy of 90% using a linear Support Vector Machine (SVM). As this streamlined methodology employs hand-crafted features to simplify the classification model, the proposed IP core uses only 1.92% of FPGA resources and 21 mW of power. The current work presents a potential design for scalable and hardware-accelerated medical imaging systems attaining minimal power with high efficiency.