Automated Brain Tumor Diagnosis and Localization Using CNN and U-Net 3D: A Radiomics Approach for Enhanced Medical Imaging Analysis

Brain tumors require precise analysis and delineation for treatment. This paper presents an automatic method of identifying brain tumors and segmentation utilizing the U-Net architecture and CNN. The method uses deep learning to identify probable tumor spots in brain MRI data. The CNN-based detection model locates aberrant tissue areas, and the following U-Net segmentation network refines the results by providing pixel-level tumor boundaries. The advantages of this approach include its capacity to handle complex tumor shapes, adjust to image fluctuations, and reduce manual labor. It assists healthcare providers in making correct and timely treatment decisions. The framework's usefulness is demonstrated by experimental findings on various datasets. It beats previous approaches by providing excellent accuracy in tumor detection and precise segmentation, making it a crucial medical tool.