Volume 2 - Issue 1, January - February 2026

The detection, segmentation, and classification of brain anomalies—ranging from malignant gliomas and metastatic tumors to ischemic strokes and demyelinating lesions—constitutes a foundational challenge in modern neuroradiology. While Magnetic Resonance Imaging (MRI) provides unparalleled soft-tissue contrast and volumetric insight, the manual interpretation of these complex, high-dimensional data streams is labor-intensive, subject to inter-observer variability, and increasingly unsustainable against the backdrop of rising global disease burden. This research report presents an exhaustive synthesis of the current state-of-the-art in machine learning (ML) paradigms applied to brain anomaly detection. We systematically evaluate the trajectory from supervised Convolutional Neural Networks (CNNs), which revolutionized semantic segmentation but remain constrained by the scarcity of voxel-level annotations, to the nascent domain of Unsupervised Anomaly Detection (UAD) leveraging Generative AI. Detailed methodological analyses are provided for emerging architectures, including Vision Transformers (ViTs) that capture longrange semantic dependencies, and Denoising Diffusion Probabilistic Models (DDPMs) that learn normative distributions of healthy anatomy to identify outliers. We critically assess benchmark performance across standard datasets such as BraTS2021, ATLAS, and the newly introduced NOVA and BMAD suites, highlighting the trade-offs between computational efficiency—where feature-adaptation networks like SimpleNet excel—and anatomical fidelity, where guided diffusion models like THOR dominate. Furthermore, we explore the implementation of hybrid systems like Swin-UNETR and Masked Bernoulli Diffusion, which attempt to reconcile the conflicting demands of 3D volumetric reasoning and GPU memory constraints. The report concludes that while supervised methods remain the gold standard for specific, well-characterized pathologies, the future of generalpurpose neuro-diagnostics lies in self-supervised, foundation-model-driven approaches capable of generalizing to open-set clinical environments.

Immanuel I, Devi Arul M, Srikanth R G, Kavitha V,"ML for Brain Anomaly Detection" International Journal of Scientific Research and Engineering Development, V2(1): Page(329-336) Jan-Feb 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.