Published Paper Details:

The early and accurate detection of brain tumors is crucial for effective treatment and improved patient outcomes. Image segmentation plays a vital role in isolating tumor regions from medical imaging modalities, and advancements in computer vision have significantly enhanced this process. This review paper provides a comprehensive overview of the latest image segmentation techniques for brain tumor detection using OpenCV, a popular open-source . Image segmentation library. We delve into various methods, including thresholding, edge detection, region-based segmentation, and machine learning approaches integrated with OpenCV functionalities. Additionally, we discuss the effectiveness, limitations, and computational efficiency of these techniques. By analyzing recent studies and developments, this review paper aims to highlight the strengths and weaknesses of different segmentation methods, offering insights into their practical applications and potential future improvements. Our review underscores the importance of continued innovation in image processing algorithms and the integration of advanced machine learning models to enhance the accuracy and reliability of brain tumor detection.