Breast Cancer Detection Using Comprising Fuzzy C-Means and Artificial Bee Colony Optimization Segmentation and Grading with Random Forest Classifier
Bibliographic record
Abstract
The integration of machine learning models into artificial intelligence has precipitated significant advancements in medical science.Notably, various programs have equipped radiologists with valuable tools to aid in medical image processing.Breast cancer stands out as the most prevalent cancer among women globally.The automated detection and classification of lesions in mammograms remain critical challenges necessitating more accurate diagnosis and meticulous examination of concerning lesions.Mammography is a pivotal diagnostic procedure for early breast cancer detection, enabling individuals to identify changes in their breasts far before they are palpable.In the relentless quest to improve patient care and tackle the prevalent ailments of our time, diverse fields such as data mining and artificial intelligence are making substantial contributions to breast cancer analysis.A groundbreaking investigation is currently focused on developing an innovative image processing technique aimed at detecting and grading breast cancer using mammogram and MRI images.This research relies on a unique image segmentation method utilizing a newly devised algorithm, coined CABC (Comprising Fuzzy C-Means and Artificial Bee Colony optimization).This inventive algorithm synergistically combines the benefits of FCM (Fuzzy C-Means) clustering and the robustness of Artificial Bee Colony (ABC) optimization.To determine the cancer stage, a random forest classifier is used, thereby enhancing the precision of the evaluation.The results stemming from the application of the CABC algorithm have demonstrated an impressive accuracy rate of 89.17%, attesting to the effectiveness of the proposed methodology.To thoroughly assess its performance, a comparative analysis has been conducted, involving other methodologies such as k-Means, Context-Based Clustering, Random Forest, and FCM individually.This rigorous evaluation employs both confusion matrix parameters and decision parameters, conclusively validating the superior performance of the proposed method.Essentially, this study exemplifies the synergistic collaboration between sophisticated image processing techniques, advanced clustering algorithms, and machine learning classifiers in refining breast cancer detection and grading.The empirical evidence presented highlights the potential of the CABC algorithm as a trailblazing instrument in this essential area of medical research.
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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 | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".