Accurate and rapid detection of brain tumors is vital for timely diagnosis and treatment planning. This paper proposes an improved computer-aided diagnosis (CAD) system enhanced with artificial intelligence (AI) techniques for automated brain tumor segmentation from magnetic resonance imaging (MRI). The proposed framework consists of three main stages: (1)
image preprocessing using spatial filters and contrast enhancement, (2) segmentation using a deep learning-based thresholding and region proposal network, and (3) post-processing based on morphological operations and adaptive masking. Experimental results on a public dataset show an average accuracy of 97% and a Dice similarity coefficient above 0.86. Compared to traditional segmentation techniques, the integration of AI significantly improves robustness, reduces
false positives, and enhances boundary precision, making this system suitable for clinical implementation.

Auteurs : Abdellatif BOUZID-DAHO

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