A Robust Automated Cervical Cancer Detection System Using Elephant Herding Optimized MCNN
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cervical cancer is a leading cause of cancer-related deaths among women, and early detection is crucial for improving survival rates. This research proposes an automated system for classifying cervical cancer using medical images. The system starts with image preprocessing, where images are resized and noise is removed using a Median Filter. Segmentation is performed using K-Means Clustering to isolate cancerous regions. The Local Binary Pattern (LBP) technique is applied for feature extraction, capturing texture patterns to distinguish normal from abnormal tissues. Classification is achieved using a Modified Convolutional Neural Network (MCNN), with optimization through the Elephant Herding Optimization (EHO) algorithm to fine-tune the model's parameters. This approach aims to assist healthcare professionals in diagnosing cervical cancer more efficiently and accurately, improving patient outcomes. The system can provide rapid, reliable results, enabling timely treatment and potentially reducing the global burden of cervical cancer.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.000 | 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 it