Breast Cancer Detection Using Convolutional Neural Networks Model
Bibliographic record
Abstract
Breast cancer is a significant disease that threatens people's health nowadays. A standard way to detect breast cancer is using radiology images by skilled physicians. However, this method causes problems in locating the cancerous area and intensive work in diagnosing and detecting histopathology images due to technical problems. The research results in the latest years can be divided into two directions, methods relying on machine learning or methods relying on deep learning. Due to the high dependency on the labeled data, we conducted two series of experiments that represent two reforming methods, analyzing a data set of a large number of breast cancer images to deal with these disadvantages. For the first series of experiments, we mutate the size of the training set using different models and compare the performances of the three models. For the second part, we do data augmentation on models to compare them before and after data augmentation and observe whether it makes these they achieve or reach the effect of the qualified model. Moreover, as the result shows, both our methods can reduce the workload in diagnosing breast cancer while maintaining or even improving the test accuracy, which benefits the follow-up work and development of the breast cancer diagnosis field.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".