A Hybrid Mobile Net-Active Learning Framework for Breast Cancer Detection Using Ultrasound Images
Bibliographic record
Abstract
Breast cancer is still one of the main global causes of death among women.Thus, detecting it early is critical.Most papers on breast cancer have used the same dataset published online under the name Kaggle.Still, in this paper, we have collected an authentic and unique ultrasound dataset of patients from the Teaching Oncology Hospital in Iraq.This paper proposes a transfer learning-based approach using the MobileNet architecture enhanced with Active Learning (MobileNet_AL) to categorize breast ultrasound images into three classes: normal, benign, and malignant.The proposed methodology effectively addresses challenges such as class imbalance and limited labeled data by integrating data augmentation, preprocessing, and iterative sample selection through Active Learning.A comprehensive dataset, combining a Kaggle dataset with a unique collected dataset, was employed to ensure balanced and diverse training data.The MobileNet model achieved an accuracy of 94.67%, outperforming state-of-the-art methods reported in the literature.The comparative analysis further demonstrated significant improvements in the recall, precision, and F1 score across all classes.These findings demonstrate the potentiality of combining transfer learning with advanced optimization methods in medical imaging.Thus, providing robust and efficient diagnostic tools for breast cancer detection.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".