Bibliographic record
Abstract
Machine learning has been applied to classify chest X-ray images into pneumonia-positive and pneumonia-negative classes to allow an early diagnosis and support medical experts’ decision about pneumonia. However, the previous attempts in this literature focus on binary classification that may not consider the possibility of uncertain information in chest X-ray images, forcing the system to make a definite decision on every instance. This may lead to the inaccurate classification of doubtful X-ray images. This research approaches Game-theoretic rough sets (GTRS) to determine three-way decisions, such as X-ray images are classified into three disjoint classes that are developed using a threshold pair. The first two classes lead to a certain decision (i.e., either pneumonia-positive or pneumonia-negative). The remaining class covers X-ray images that lack the crucial information to make inferences about them. GTRS obtains the suitable threshold pair by formulating a trade-off between accuracy and coverage criteria of the proposed model. We achieve a 96.25% accuracy score while covering 64.01% of the test set for certain decision. The experiment results show that the proposed model secures a better classification performance than 0.5-probabilistic rough sets, Pawlak’s rough sets, and machine learning models.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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 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".