https://iieta.org/Journals/ISI/Archive/Vol-30-No-3-2025
Bibliographic record
Abstract
Breast cancer is the most common and lethal cancer among women across the globe, and supporting early detection through accurate diagnostic measures would save many lives.However, existing diagnostic techniques often encounter problems concerning accuracy and reliability, hence, they are ineffective.This paper presents a new multi-classifier machine learning technique for breast cancer diagnosis using the integration of conventional machine learning (ML) and deep learning (DL) paradigms.The model employs a two-step process: The first step is feature selection using a random forest (RF) to do dimensionality reduction, and feature selection eliminates features that are not useful; the last step is classification using a convolutional neural network (CNN).The hybrid model is then tested using a Wisconsin breast cancer data set.Evaluation criteria for the key performance indicators include accuracy, precision, recall rate, F1-score, and AUC ROC.As the results have revealed, the hybrid model is higher than the traditional methods like logistic regression (LR) with an accuracy of 94.5%, a precision of 92.8%, recall of 95.0%, an F1score of 93.8% and an AUC-ROC of 0.97.This study demonstrates that integrating human readers into the evaluation process can enhance the reliability and efficiency of clinical breast cancer detection and, hence, contribute to developing diagnostic techniques in online medical image analysis.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.541 | 0.595 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".