code.cpp

#include <CL/sycl.hpp>
#include <Eigen/Dense>
#include <arrayfire.h>
#include <opencv2/opencv.hpp>

namespace sycl = cl::sycl;

int main() {
  // Specify the path to the MRI image file
  std::string imagePath = "mriimage.jpg";

  // Load the MRI image using OpenCV
  cv::Mat image = cv::imread(imagePath, cv::IMREAD_GRAYSCALE);

  // Create a SYCL queue
  sycl::queue q(sycl::default_selector{});

  // Define the kernel function for tumor detection
  auto tumorDetectionKernel = [&](sycl::handler& cgh) {
    // Get write access to the image data
    auto imgAccessor = imgBuffer.get_access<sycl::access::mode::write>(cgh);

    // Define a parallel range based on the image dimensions
    sycl::range<2> imageSize(image.cols, image.rows);

    // Execute the tumor detection kernel
    cgh.parallel_for(imageSize, [=](sycl::id<2> id) {
      // Get the pixel value at the current position
      uint8_t pixel = image.at<uint8_t>(id[1], id[0]);

      // Perform tumor detection logic
      bool isTumorPixel = ...; // Implement your tumor detection algorithm here

      // Update the image with the tumor detection result
      uint8_t resultPixel = isTumorPixel ? 255 : 0;
      imgAccessor[id] = resultPixel;
    });
  };

  // Submit the tumor detection kernel to the SYCL queue
  q.submit([&](sycl::handler& cgh) {
    // Create a buffer for the image data
    sycl::buffer<uint8_t, 2> imgBuffer(image.data, sycl::range<2>(image.cols, image.rows));

    // Associate the buffer with the kernel function
    cgh.set_args(imgBuffer);

    // Execute the tumor detection kernel
    cgh.single_task(tumorDetectionKernel);
  });

  // Wait for the queue to finish
  q.wait();

  // Get the updated image data from the buffer
  cv::Mat detectedImage(image.rows, image.cols, CV_8UC1, imgBuffer.get_pointer());

  // Display the detected tumor image
  cv::imshow("Detected Tumor Image", detectedImage);
  cv::waitKey(0);

  return 0;
}