Image processing techniques for the detection of brain tumours

Abstract

Introduction: This paper is centered around advancing brain image analysis through the introduction and evaluation of advanced methods. Methods: With the overarching goal of enhancing both image quality and disease classification accuracy, the paper sets out to address crucial aspects of modern medical imaging. The research's trajectory begins by laying a strong foundation through an in-depth exploration of the principles governing Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). This understanding serves as a springboard for the subsequent phases, wherein image quality improvement takes center stage. Results: By employing cutting-edge image processing techniques, the research aims to reduce noise and enhance image clarity, thereby setting the stage for more reliable and precise analysis. The second phase involves segmentation, a pivotal step in brain image analysis. Various segmentation methods will be assessed to determine their efficacy in accurately identifying distinct brain structures. Finally, the paper delves into the realm of deep learning, particularly leveraging CNN, to classify brain images based on disease types. This sophisticated approach holds promise for refining disease identification accuracy by identifying nuanced patterns within the images. Conclusion: Overall, the research aspires to modernize and elevate the field of brain image analysis, ultimately contributing to improved medical diagnostics and insights.