Non-invasive estrous cycle classification in mice using convolutional neural networks
Bibliographic record
Abstract
Abstract Accurately identifying the estrous cycle phases in laboratory mice is essential for neurological research, reproductive studies, and breeding programs. Here, we introduce a non-invasive approach using Convolutional Neural Networks (CNNs) to classify estrous stages from external genital images, with cytological smears serving as the reference standard for labeling. Images were curated, preprocessed, and augmented to ensure consistency and generalization. Seven pre-trained CNNs and a lightweight custom architecture, Repro Cycle Net (RCN), were evaluated. All models achieved accuracies above 75%, with RCN exceeding 83% and showing the lowest test loss, highlighting its efficiency despite a simple four-layer design. Saliency map analyses revealed that classification relied on perivaginal features, while irrelevant regions such as the fur area were largely ignored. Importantly, binary classification of estrus versus non-estrus directly informs mating feasibility, underscoring the immediate utility of this method for reproductive studies and colony management. This work demonstrates that combining deep learning with external genital observation enables efficient and reproducible estrous monitoring, supporting both experimental reliability and animal welfare.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".