International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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SeruNet (Smart Explainable Platform for Radiological Understanding): A Unified Multi‑Modal AI System for Neurological Disorder Detection

Author(s) Ms. Serra Aksoy
Country Germany
Abstract Neurological diseases affect over one billion people globally, yet countless communities still lack basic access to specialist care, especially during emergencies. This work presents the first unified artificial intelligence platform capable of detecting and analyzing four major neurological conditions (brain tumors, strokes, Alzheimer's disease, and multiple sclerosis) within a single, web-accessible system. SeruNet platform addresses critical gaps in neurological care delivery by integrating condition-specific expert systems with advanced explainable AI techniques including one of the first documented application of XRAI (eXplanation with Region Attribution Integration) for neurological imaging. The unified architecture combines 2D and 3D analysis capabilities across multiple imaging modalities while maintaining specialized accuracy for each neurological condition. Key innovations include region-based attribution that aligns with clinical reasoning, a novel two-stage multiple sclerosis risk prediction model, and comprehensive bias-aware monitoring systems. Web-based deployment eliminates infrastructure barriers, enabling immediate access through standard browsers without specialized hardware requirements. This unified approach represents a paradigm shift from fragmented, condition-specific AI systems toward integrated, accessible neurological diagnostics designed for global health equity and immediate clinical deployment.
Keywords SeruNet, XRAI, Neurological imaging, Multiple imaging modalities, Region-based attribution, Web-based deployment
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 4, July-August 2025
Published On 2025-08-03
DOI https://doi.org/10.36948/ijfmr.2025.v07i04.52891
Short DOI https://doi.org/g9vzmf
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E-ISSN 2582-2160
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