International Journal of Advanced Research in Computer and Communication Engineering

Abstract: This paper presents a novel Neurosymbolic AI framework designed to enhance the accuracy and explainability of brain tumour diagnosis. By combining deep learning architectures (VGG16 for classification and U-Net for segmentation) with a symbolic genomic rule engine, the system integrates structural MRI data with molecular biomarkers such as IDH mutation and MGMT promoter methylation status. This multi-modal approach achieves high-fidelity risk assessments while providing clinicians with "white-box" explainability through Grad-CAM heatmaps and guideline-based treatment recommendations. Additionally, the system features an interactive Student Learning Lab and a federated learning hub to support decentralised training and medical education.

Keywords: Neurosymbolic AI, Brain Tumour Diagnosis, Explainable AI (XAI), Multi-modal Fusion, Deep Learning, Genomic Reasoning.

Downloads: | DOI: 10.17148/IJARCCE.2026.15110