Optimization of Breast Cancer Classification Using Faster R-CNN
Bibliographic record
Abstract
Breast cancer results from aberrant cell division in the breast and leads to the formation of tumors.The modern lifestyle, which is instant and rarely exercises, is the main driving force for this disease.Therefore, this study aims to diagnose by recognizing the specific characteristics of cancer in a benign or malignant class in the breast area.This study approach uses the deep learning technology model Faster R-CNN and dataset Mammographic Image Analysis Society (MIAS).This model requires unique image characteristics to recognize and produce a higher accuracy value.Furthermore, this study proposes optimizing an image segmentation approach using Matlab, ImageJ, and Python software to enrich cancer-specific images.This approach plays a vital role in increasing the accuracy of cancer detection.The results of this study before optimization have an accuracy rate of 63.47% using a smartphone camera; after optimization, the highest accuracy value becomes 90.43%, therefore 9.57% requires further examination by a specialist.Based on these results, these results help assist radiologists in making decisions about the results of the initial examination of breast mammogram data.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".