Brain Tumor Diagnosis Support System: A Decision Fusion Framework
Bibliographic record
Abstract
The early and accurate detection of brain tumors is important in providing effective and efficient therapy and thus can result in increased survival rates. Current image-based tumor detection and diagnosis methods depend heavily on the interpretation of the neuro specialists and/or radiologists. Therefore, it is quite possible for the interpretation process to be time-consuming, and prone to human error and subjectivity. Automatic detection and classification of brain tumors have the potential to achieve efficiency and higher degree of predictable accuracy. However, it is well established that the accuracy performance of automatic detection and classification techniques varies from technique to technique, and tends to be image modality dependent. Thus, it is prudent to explore the variability in the performance of these techniques as a means to achieve consistent high accuracy performance. This paper presents a framework for fusing multiple tumor classifiers. The fusion process is based on the Dempster Shafer evidence fusion theory. Several tumor classifiers are employed. Experimental results will be presented to validate the efficiency of the proposed framework. It is concluded that fusing the classification decisions made by the various classifiers it is conceivable that efficient and consistent high accuracy classification performance can be attained.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".