Design of FPGA based Custom IP Core to Detect the Edges of Brain Tumors

In the realm of medical imaging, identifying and classifying tumors using light weight machine learning algorithms require edge detection procedure. This renders the process of edge detection as a task of utmost importance which makes a difference between a right and wrong diagnosis. Due to its emergent nature , prompt and accurate diagnosis necessitates parallel and real-time processing. The process is made easier by the reconfigurable and parallel processing capabilities of field programmable gate array (FPGA). This paper presents the design and implementation of a custom Intellectual Property (IP) core for processing edge detection algorithm on FPGA. The proposed IP core is made to effectively use FPGA hardware acceleration to compute the edges of brain tumors. The scalable design of the core makes it simple to incorporate it into a range of FPGA-based systems and applications. The design of such custom IP core is achieved with the help of high level synthesis (HLS) which is then integrated and implemented on PYNQ Z2 board. The core is synthesized and tested to determine the performance metrics such as throughput, resource utilization, and power consumption. Effectiveness of the proposed IP core is demonstrated by experimental results. This IP core is designed in such a way that it processes the image and offers an adaptable and effective edge detection solution. This in turn opens the doors for integration into a range of FPGA-based real time applications and systems in various domain such as industrial automation, robotic surgery, and autonomous vehicles.