Enhanced AI-Based Diagnostic Framework: Ensemble Modeling for Multi-Orientation MRI Classification of Brain Tumors and Multiple Sclerosis
Bibliographic record
Abstract
Brain tumors and multiple sclerosis (MS) are complex medical conditions characterized by overlapping clinical and imaging features, posing significant challenges in accurate diagnosis. Building upon our previous work, which utilized axial MRI images for classification into three categories—normal, brain tumor, and MS—this study extends the methodology to incorporate sagittal and coronal orientations. Individual convolutional neural network (CNN) models are trained for each orientation, and their outputs are integrated using an ensemble framework with a voting mechanism. This approach leverages the complementary spatial information provided by multi-orientation analysis to enhance diagnostic precision. Experimental evaluations demonstrate that the ensemble model achieves superior classification accuracy and robustness in contrast to the single-orientation approach. This piece emphasizes the vital role that multi-orientation MRI analysis plays in mitigating diagnostic ambiguities and advancing the reliability of AI-driven medical imaging frameworks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".