A two-stage cervical pathology cell detection model based on YOLOv7x and K-means
Bibliographic record
Abstract
As a malignant tumour that seriously threatens women's health, early diagnosis of cervical cancer is crucial to improve the cure rate. Cervical pathological cell detection is a key link in the early diagnosis of cervical cancer, but the traditional detection methods have the problems of low efficiency and accuracy relying on manual work. In this study, we combined the YOLOv7x target detection model with K-means clustering algorithm to construct a two-stage model YOLOv7x-CH for cervical pathological cell detection. To address the problems in the detection of cervical pathological cells of High-grade Squamous Intraepithelial Lesion (HSIL) category, we firstly constructed the To address the problems in the detection of HSIL, we first constructed the YOLOv7x baseline model, extracted the nucleoplasmic ratio features based on fine segmentation and grey scale symbiosis matrix texture features from the HSIL samples, and then constructed the two-stage model YOLOv7x-CH through K-means clustering. The experimental results show that compared with the baseline model, the two-stage model YOLOv7x-CH can improve the accuracy of cervical pathological cell detection from 0.56 to 0.813, and the average accuracy AP from 0.556 to 0.591, which effectively improves the accuracy of cervical pathological cell detection, and provides a more reliable technical support for the early diagnosis of cervical cancer.
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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 | 0.002 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".