Name: FPGA-Based Brain Tumor Detection from MRI Using 3x3 Convolution Soft IP Core with Stride 1
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FPGA-Based Brain Tumor Detection from MRI Using 3×3 Convolution Soft IP Core with Stride 1

Source : Verilog HDL

Base Paper Abstract:

This paper presents an efficient FPGA-based system for automatic brain tumor detection from MRI images using a 3×3 convolutional edge detection method with stride 1. The proposed architecture is developed as a soft IP core in Verilog HDL and synthesized on a Xilinx Zynq 7000 FPGA platform. The system applies a customized 3×3 convolution kernel over each MRI image with stride 1, ensuring that every pixel is processed and fine image details are preserved for accurate tumor detection. Edge detection results are used to segment and highlight abnormal regions, and a thresholding mechanism is employed to differentiate between normal and abnormal images. Hardware resource utilization—including look-up tables (LUTs), flip-flops (FFs), and power consumption—is analyzed after synthesis to verify system efficiency. Experimental results confirm that the proposed FPGA implementation provides real-time processing and reliable brain tumor detection with low power usage, making it suitable for portable and embedded medical devices. The stride 1 approach guarantees maximum detection accuracy and detailed edge representation in all test cases.

List of the following materials will be included with the Downloaded Backup:

1. Source code ( Modelsim/ Xilinx/ Quartus/ DSCH3/ Microwind)

2. Existing and Proposed Project Comparison with output video

3. Basic Documentation (20 to 30 Pages):

3.1 Proposed Title

3.2 Proposed Abstract

3.3 Advantages & Disadvantages

3.4 Improvement of this Project

3.5 Existing System with Notes

3.6 Proposed System with Notes

3.7 Literature Survey

3.8 Software Related Notes

3.9 VLSI and HDL Language / Tanner Notes

3.10 References & Reference Paper for More Pages

4. Online Support ( Any Desk / Zoom / Google Meet)

Objectives:

To design and implement an efficient FPGA-based system for automatic detection of brain tumors in MRI images.
To develop a soft IP core in Verilog HDL that performs 3×3 convolutional edge detection with stride 1, preserving fine image details.
To apply the convolutional edge detection method for segmenting and highlighting abnormal brain regions.
To integrate a thresholding mechanism for accurate differentiation between normal and abnormal MRI images.
To synthesize the proposed architecture on a Xilinx Zynq 7000 FPGA platform and analyze hardware resource utilization, including LUTs, FFs, and power consumption.
To validate the system’s capability to deliver real-time processing and reliable tumor detection performance suitable for portable and embedded medical devices.
To ensure that the stride 1 convolution approach provides maximum detection accuracy and detailed edge representation in all test cases.

Software Implementation:

Modelsim
Vivado

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