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Automatic Brain Tumor Detection and Classification Using UNET and Optimized Support Vector Machine

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Abstract

Brain tumors, characterized by the growth of aberrant and unregulated cells within the skull, pose a significant threat to normal brain function and, if malignant, can present life-threatening consequences. Timely identification and diagnosis through computed tomography (CT) or magnetic resonance imaging (MRI) are pivotal for effective intervention. This study introduces a streamlined image-processing strategy aimed at precisely localizing and identifying brain tumor regions. The methodology involves successive phases including preprocessing, edge detection, and segmentation. The initial phase converts the original image into a grayscale format while incorporating essential noise-reduction techniques. Subsequently, image enhancement methods are applied to improve the image quality. The pivotal step of segmentation employs a UNET trained on meticulously defined tumor mask regions, facilitating accurate delineation of tumor boundaries. The proposed methodology demonstrates exceptional performance metrics, achieving an impressive accuracy of 99.67%. Furthermore, it attains notably high specificity and precision scores of 99.73% and 98.79%, respectively. The sensitivity metric also demonstrates robust performance at 97.43%. This comprehensive approach offers a promising avenue for precise and automated brain tumor detection and classification in medical imaging, providing clinicians with a reliable tool for early and accurate diagnosis, critical for timely intervention and patient care.

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Authors and Affiliations

  1. Department of Information Technology, Malla Reddy Engineering College for Women (UGC-Autonomous), Hyderabad, India

    Subba Reddy Borra, M. Krishna Priya, M. Taruni, K. Samhitha Rao & M. Sanjana Reddy

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  1. Subba Reddy Borra
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  2. M. Krishna Priya
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Borra, S.R., Priya, M.K., Taruni, M. et al. Automatic Brain Tumor Detection and Classification Using UNET and Optimized Support Vector Machine. SN COMPUT. SCI. 5, 540 (2024). https://doi.org/10.1007/s42979-024-02881-7

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