Transfer learning-enhanced CNN model for integrative ultrasound and biomarker-based diagnosis of polycystic ovarian disease
Bibliographic record
Abstract
Polycystic Ovarian Disease (PCOD), also known as Polycystic Ovary Syndrome (PCOS), is a prevalent hormonal and metabolic condition primarily affecting women of reproductive age worldwide. It is typically marked by disrupted ovulation, an increase in circulating androgen hormones, and the presence of multiple small ovarian follicles, which collectively result in menstrual irregularities, infertility challenges, and associated metabolic disturbances. This study presents an automated diagnostic framework for PCOD detection from transvaginal ultrasound images, leveraging an Enhanced [Formula: see text] convolutional neural network architecture. The model incorporates attention mechanisms, batch normalization, and dropout regularization to improve feature learning and generalization. Bayesian Optimization was employed to fine-tune critical hyperparameters, including learning rate, batch size, and dropout rate, ensuring optimal model performance. The proposed system was trained and validated on a curated ovarian ultrasound image dataset, applying data augmentation and SMOTE techniques to address class imbalance. Experimental evaluation demonstrated that the Enhanced [Formula: see text] model achieved a classification accuracy of 94.8%, sensitivity of 93.2%, specificity of 95.5%, precision of 94.0%, and an F1-score of 93.6% on the independent test set. Interpretability was enhanced through Grad-CAM visualization, which effectively localized diagnostically significant regions within the ultrasound images, corroborating clinical findings. These results highlight the potential of the proposed deep learning-based framework to serve as a reliable, scalable, and interpretable decision-support tool for PCOD diagnosis, offering improved diagnostic consistency and reducing operator dependency in clinical workflows.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".