Unveiling Breast Tumor Characteristics: A ResNet152V2 and Mask R-CNN Based Approach for Type and Size Recognition in Mammograms
Bibliographic record
Abstract
As one of the most prevalent and lethal diseases afflicting women today, breast cancer detection remains a pivotal area of focus.Although mammogram images, exploited in Computer-Aided Design (CAD) systems, provide an early detection avenue, their reliability for accurate recognition of tumor density types and sizes, particularly in type C and D breasts, is questionable.To address this challenge, a novel approach for tumor identification, categorization, and size estimation in various breast types is put forth in this study.In the proposed model, features are extracted from a mammographic image analysis dataset using a pre-trained Convolutional Neural Network (CNN) architecture for left-right comparison, followed by the deployment of ResNet152V2 for distinguishing between the four mammogram types (A, B, C, and D).Subsequently, normal, and abnormal breasts are differentiated within the mammogram images.The final step employs a Mask Region-Based Convolutional Neural Network (Mask R-CNN) to discern malignant from benign tumors and to estimate tumor size.The experimental outcomes demonstrate an impressive 100% overall accuracy in type comparison using ResNet152V2, thereby substantiating its viability as a model for mammogram type detection and classification.This study thus provides a compelling argument for the application of ResNet152V2 in the context of breast cancer detection and diagnosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".