Pap Smear Image Classification with Efficient Weight Regularization for Cervical Cancer Diagnosis
Bibliographic record
Abstract
Pap smears, also known as pap tests, can identify abnormal cells early that develop cervical cancer, allowing for timely intervention and treatment.Even though the incidence rate is reduced in this modern era, it poses a significant risk to human life and should be taken very seriously.An accurate and rapid system for classifying pap smear images is necessary to provide appropriate therapy.Deep Neural Networks (DNNs) have garnered much interest in recent years and have shown outstanding categorization results in computer vision.An efficient Pap Smear Image Classification (PSIC) system with Efficient Weight Regularization (EWR) in DNN is presented in this study.The main problem with neural networks is that they have large weights that overfit the training data.To overcome this difficulty, the EWR approach is employed to penalize the large weights using grid search.The proposed non-invasive support system detects pap-smear images with cancerous cells.The HERLEV dataset comprises 675 digitized abnormal images, and 242 normal images are utilized for the classification task.When using the capabilities of the EWR-DNN combination, the proposed PSIC system can work at its absolute best.The concepts described in this study also provide a possible path to increase the categorization accuracy of all medical diagnoses.Results show that the PSIC system, which employs EWR approach achieves 98.9% classification accuracy, 99.3% specificity and 98.5% sensitivity using a regularization parameter of 10 -3 .The comparison study with other deep learning models such as VGG, ResNet, AlexNet and GoogleNet also shows the superior performance of the PSIC system.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".