This review delves into the fusion of machine learning, medical image processing, and computational modeling for detecting and classifying cerebral aneurysms. It begins with an overview of machine learning principles in medical diagnostics, specifically focusing on cerebral aneurysms.

Essential techniques in medical image processing are then explored, emphasizing their role in refining diagnostic accuracy. The importance of early detection is emphasized, highlighting its critical impact on reducing risks associated with cerebral aneurysms.

The complexities of image segmentation are discussed, covering various methods like region-based and boundary-based approaches. Feature extraction techniques are elucidated for their crucial role in refining diagnostic precision, accompanied by real-world case studies showcasing their efficacy in clinical settings.

The review concludes with an exploration of Computer-Aided Diagnosis (CAD) systems and mathematical modeling, showcasing their integration and synergies with clinical expertise. This final section underscores the potential of computational models to revolutionize cerebral aneurysm detection and classification practices.

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