Published Paper Details:

Brain tumor detection and segmentation are critical tasks in medical imaging analysis for diagnosis and treatment planning. In recent years, computer vision techniques, particularly those implemented using the OpenCV library, have shown promising results in automating these processes. This paper presents a comprehensive review and analysis of brain tumor detection and segmentation methods employing OpenCV. Various techniques, including image preprocessing, feature extraction, and segmentation algorithms, are explored and compared in terms of their effectiveness, computational efficiency, and applicability to different types of brain tumors. Furthermore, challenges and opportunities in this field are discussed, along with potential future research directions. The findings of this study contribute to the advancement of automated brain tumor detection and segmentation systems, ultimately aiding healthcare professionals in improving diagnosis accuracy and treatment planning.