Voxelmind: An Explainable Radiomics and Clinical Intelligence Framework for Automated Neuro-Imaging and Prognostic Decision Support
Mr. J Noor Ahamed1, Aiswarya M P2
1Assistant professor, Department of Computer Applications, Nehru College of Management, Coimbatore, Tamil Nadu, India, ncmnoorahamed@nehrucolleges.com
2Student of II MCA, Department of Computer Applications, Nehru College of Management, Coimbatore, Tamil Nadu, India, aiswarya19022003@gmail.com
ABSTRACT
VoxelMind is an explainable artificial intelligence (AI) framework designed to deliver integrated neuro-imaging analytics and prognostic prediction for clinical decision support. The growing complexity and volume of neuro-imaging data from modalities such as MRI, CT, and DICOM scans create challenges for manual interpretation and consistent diagnosis. VoxelMind addresses these challenges by integrating automated image preprocessing, lesion segmentation, and radiomic feature extraction within a unified and scalable platform. Quantitative features, including lesion area, estimated volume, tissue heterogeneity, and symmetry index, are utilized to compute a complexity score for disease severity assessment and risk stratification.
The framework further incorporates a machine learning–based prognostic module to estimate disease progression and survival probability, supported by explainable outputs through feature importance analysis. A Natural Language Processing component analyzes unstructured clinical notes to enhance diagnostic insight, while interactive visualization dashboards, electronic health record integration, and automated report generation support transparent clinical workflows. Deployed as an offline-capable web-based system, VoxelMind is suitable for both advanced and resource-constrained healthcare environments, demonstrating the effectiveness of explainable AI in improving diagnostic accuracy and clinical decision-making.
Keywords— Explainable Artificial Intelligence, Neuro-Imaging Analytics, Radiomics, Prognosis Prediction, Clinical Decision Support, Medical Image Processing.
