Region of Interest-Based Breast Cancer Detection with Oversampling Technique
Bibliographic record
Abstract
Breast cancer detection using medical imaging remains a challenging task due to the large volume of mammograms and the inherent class imbalance in datasets.This study proposes a novel regions of interest (ROIs)-based approach using RSNA screening mammography breast cancer detection dataset.By focusing on specific ROIs within the mammograms, the computational load is reduced while allowing the model to concentrate on the most critical areas.Additionally, SMOTE Tomek Link is applied to mitigate the class imbalance by generating synthetic samples for the minority (cancerous) class and removing noisy or overlapping samples.Three dataset splits were created: Split 1 (5:1 ratio of normal to cancer cases), Split 2 (3:1), and a fully balanced Random Under-Sampling (RUS) dataset.Various CNN models, including InceptionV3, ResNet152V2, DenseNet201, and EfficientNetB7, were evaluated on different dataset splits.Our results demonstrate that the EfficientNetB7 model, in conjunction with ROI extraction and SMOTE Tomek Link, achieves the highest accuracy of 97.41% on the Split 2 dataset, highlighting the effectiveness of these preprocessing techniques in enhancing deep learning-based 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 | 0.002 |
| 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 | 0.001 | 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".