MUMBAI, India, Aug. 17 -- Intellectual Property India has published a patent application (202621071075 A) filed by Veena Kewalram Katankar Nerkar; Prof. Dr. Ktv Reddy; and Dr. Pravin Shrawanji Nerkar on June 08, 2026, for 'Autonomous Hybrid Deep Learning Framework For Context-Aware Brain Tumor Detection, Casual Neuro-Diagnostic Reasoning, And Explainable Multimodal Clinical Intelligence Scenarios'.
Inventor includes Veena Kewalram Katankar Nerkar.
The application for the patent was published on August 14, 2026, under issue no. 33/2026.
Abstract: The present invention discloses an autonomous hybrid deep learning framework for intelligent detection, classification, progression analysis, and explainable diagnosis of brain tumor diseases using multimodal magnetic resonance imaging data and contextual neuro-diagnostic intelligence. The invention introduces a sequential multi- stage analytical architecture integrating spectral encoding, graph-based pathological reasoning, causal radiomic synthesis, uncertainty-guided neuro cognition, and explainable artificial intelligence for advanced neuro-oncology diagnostics. The proposed system initially acquires heterogeneous brain MRI modalities including Tl- weighted, T2-weighted, FLAIR, and contrast-enhanced MRI scans along with contextual patient metadata. A Neuro-Quantized Spectral Encoding Network (NQSE- Net) performs adaptive neuro-spectral decomposition and contextual quantization to generate tumor saliency tensors capable of enhancing abnormal tumor boundaries, suppressing imaging noise, and amplifying low-contrast pathological signatures. The generated saliency tensors are processed through a Hierarchical Graph Transformer for Pathological Context Learning (HGT-PathFormer), wherein pathological brain regions are transformed into contextual neurograph structures for modeling edema propagation, necrosis interaction, infiltrative tumor behavior, vascular deformation, and long-range cortical dependencies. Further, a Dynamic Causal Radiomic Fusion Neuro-Synthesizer (DCRF-NeuroSynth) performs causal biomarker inference and temporal neuro-synthesis to generate progressive tumor behavior vectors associated with tumor aggression and progression trajectories. The framework additionally integrates an Uncertainty-Guided Meta Cognitive Tumor Reasoning Network (UQ-MetaCortex) configured to perform Bayesian uncertainty calibration and confidence-adaptive diagnostic reasoning for reducing false-positive predictions and improving diagnostic reliability. An Autonomous Interpretable Tumor eXplainability NeuroScope (AITX-NeuroScope) subsequently generates clinically interpretable neuro-oncology diagnostic reports through pathological reasoning visualization, confidence-guided lesion mapping, biomarker relevance analysis, and multimodal evidence reconstruction. The invention thereby enables highly accurate, explainable, and context-aware autonomous brain tumor diagnosis with improved classification precision, tumor progression estimation, clinical transparency, and intelligent medical decision support suitable for real-time healthcare deployment and advanced neuro-diagnostic applications.
Disclaimer: Curated by HT Syndication.
