Pneumonia Detection Enhanced by Conditional Generative Adversarial Networks cGAN Addressing Class Imbalance with High Quality Synthetic Data
Bibliographic record
Abstract
Pneumonia has recently reported the highest number of deaths in the entire world. Consequently, diagnostic procedures ought to be precise. Conversely, the problem of class imbalance is a universal issue in medical image classification that may result in biased models that do not perform well in underrepresented classes. The issue of class disproportion is successfully addressed in the work under consideration, as a cGAN generates high-quality synthetic images of the minor classes, allowing for the creation of a balanced dataset that, in turn, leads to high sensitivity of the model and consequently improves its overall performance. This paper presents a new Conditional Generative Adversarial Network (cGAN) architecture to improve the detection of pneumonia in the provided chest X-rays. To mitigate the issue of data imbalance in the dataset, this work suggests a conditional GAN-based augmentation process for synthetic X-ray images by producing clinically viable and label-coherent synthetic X-ray images. The framework also includes validation achieved through the application of either SSIM/FID or balanced training, which improves the accuracy of pneumonia detection and leads to diagnostic conclusions unlike those of existing methods. Current approaches are less successful compared to this study, as the system achieves 96.5% accuracy, 95.2% sensitivity, and 96.1% specificity, with a high F1 score. The suggested framework has a good performance scale, making it applicable in medical applications. This paper demonstrates the capacity of cGAN to develop pneumonia diagnosis machines that are feasible and user-friendly in healthcare institutions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".