Authors - Nilay Shah, Darsh Pandya, Nisarg Patel, Rudra Shah, Umang Shah, Dhaval Patel, Priteshkumar Prajapati Abstract - Early and accurate detection of brain tumors is a critical requirement in modern clinical diagnostics, as it directly affects treatment planning, disease prognosis, and patient survival rates. The rapid increase in the availability and complexity of medical imaging data has intensified the need for reliable computer-aided diagnosis (CAD) systems to assist radiologists in consistent and precise tumor identification. Among various CAD techniques, medical image segmentation plays a pivotal role in differentiating abnormal tumor tissue from healthy brain structures in diagnostic images. This paper presents an automated brain tumor detection framework based on medical image analysis, implemented using a MATLAB-based graphical user interface. The proposed system processes Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans through a structured processing pipeline that includes image acquisition, noise reduction, contrast enhancement, feature-based segmentation, and tumor region visualization. The segmentation methodology is designed to accurately localize tumor boundaries while minimizing false-negative detections, which is a crucial requirement for clinical decision-making. The developed interface enables interactive visualization of segmented regions, allowing efficient analysis without the need for extensive computational expertise. The proposed framework offers a user-friendly and computationally efficient platform that reduces reliance on manual interpretation and improves diagnostic repeatability across clinical environments. The novelty of this work lies in the seamless integration of automated tumor detection, structured segmentation techniques, and real-time visual interpretation within a unified MATLAB-based environment, providing a practical and accessible CAD solution without dependence on complex hardware or deep learning infrastructures. Experimental observations indicate that the system enhances analysis efficiency and supports medical professionals in making faster, more reliable, and time-effective diagnostic decisions.
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