NeuroCAM-X: An Explainable Hybrid AI Framework for Brain Tumor Classification Using MRI and Clinical Reports with Advanced Tumor Analytics

Abstract

Brain tumor classification from Magnetic Resonance Imaging (MRI) is a critical task in medical diagnostics that demands both high accuracy and clinical interpretability. This research presents NeuroCAM-X, a novel explainable hybrid artificial intelligence framework that integrates deep learning-based MRI image analysis with Optical Character Recognition (OCR)-enabled clinical report interpretation for comprehensive brain tumor diagnosis. The system employs an EfficientNet-B0 architecture achieving 97.0% classification accuracy on a dataset of 7,023 MRI images. Unlike conventional approaches that rely solely on imaging data, NeuroCAM-X implements a hybrid decision engine that cross-validates MRI predictions with OCRextracted clinical findings, achieving an 87.5% agreement rate for diagnostic consistency. The framework incorporates multiple Explainable AI (XAI) techniques—Grad-CAM, SHAP, and LIME—to provide complementary visual interpretations of model predictions with 94.3% alignment to expert-identified tumor regions. In addition, the system includes automated tumor analytics for quantitative assessment and staging to support clinical decision-making. A production-ready web application provides patient management, interactive diagnostic visualization, and automated report generation. Preliminary clinical evaluation demonstrated high physician trust (4.2/5.0) and satisfaction (4.4/5.0), indicating the framework's potential for clinical deployment. This work addresses critical gaps in medical AI by combining accurate classification, multimodal data integration, explainable AI, quantitative analytics, and clinical decision support within a unified framework suitable for real-world healthcare applications