NeuroShield: AI-Driven Brain Tumor Detection and Risk Assessment

Author(s)	Koyyada Jashvanth, Mallarapu Venkat Sai, N Musrat Sultana, Dr K Rajitha, R Mohan Krishna Ayyappa
Country	India
Abstract	This paper presents a deep learning-based system for automated brain tumor detection and risk assessment using a hybrid model that combines ResNet50 CNN and Swin Transformer V2-B. The system accurately identifies glioma, meningioma, and pituitary tumors from MRI scans, estimates tumor size (area, diameter, perimeter), categorizes tumors by size, and assigns risk levels. It also generates detailed clinical reports. Trained on public datasets, the model achieves high accuracy, sensitivity, and specificity, demonstrating its potential to enhance diagnostic precision, support clinical decision-making, and streamline radiological workflows.
Keywords	Brain Tumor Diagnosis, Deep Learning, CNN, Swin Transformer, MRI Classification, Tumor Size Estimation, Risk Assessment.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-05-30
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.46487
Short DOI	https://doi.org/g9mtst

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International Journal For Multidisciplinary Research