A Confidence-Weighted Rule-Based Framework for Multimodal Brain Lesion Classification Using MRI and MRS
Bibliographic record
Abstract
Differentiating brain tumors from tumor-like lesions is a persistent clinical challenge due to their overlapping imaging features on conventional radiological scans.Tumor-like lesions such as demyelinating diseases, infections, or post-traumatic changes often mimic neoplastic growths in appearance, leading to potential misdiagnosis and inappropriate treatment decisions.To address this issue, we propose a novel machine-learning-based diagnostic framework that integrates Magnetic Resonance Spectroscopy (MRS) and structural Magnetic Resonance Imaging (MRI) through a confidence-weighted fusion strategy: Final Diagnosis = 0.7 × MRS + 0.3 × MRI.This weighting reflects the higher metabolic specificity of MRS, while retaining MRI's anatomical detail.Each modality is processed through a specialized pipeline.The MRS pipeline involves image-to-numeric transformation, noise filtering, metabolite concentration-based feature extraction, expertguided feature selection, and a rule-based classifier.The MRI pipeline includes skull stripping, a novel Dynamic Image Thresholding method, multidimensional feature extraction (statistical, volumetric, shape-based), and correlation-based feature selection with a rule-based classifier.Our integrated system achieved 90% classification accuracy on a clinically validated dataset, effectively distinguishing between tumors and tumor-like lesions.Despite the small dataset (n=50) from a single center, stratified cross-validation yielded consistent results (90% accuracy), demonstrating robustness.Future external validation is planned.By introducing a confidence-informed multimodal fusion strategy, the framework provides both high diagnostic accuracy and interpretability, supporting more reliable and informed clinical decision-making in neuro-oncology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".