Hybrid Feature Selection Using the Firefly Algorithm for Automatic Detection of Benign/Malignant Breast Cancer in Ultrasound Images
Bibliographic record
Abstract
The incidence rate of breast cancer (BC) is progressively increasing worldwide, and early diagnosis can help reduce the mortality rate.Ultrasound imaging, a cost-effective imaging technique, is widely used for initial screening of patients suspected of having breast cancer.Categorizing breast ultrasound images into benign and malignant classes is crucial for planning appropriate treatment strategies to combat BC.This research proposes a Convolutional Neural Network (CNN) framework to classify breast ultrasound images.This framework comprises the following stages: (i) image collection and resizing, (ii) CNN segmentation to extract the cancerous region, (iii) deep feature mining, (iv) extraction of handcrafted features, (v) selection of optimal features based on the Firefly algorithm (FA) and serial concatenation of features to create the feature vector, and (vi) performance evaluation and validation.The proposed classification task is executed using (i) deepfeature-based classification and (ii) integrated deep and handcrafted (hybrid) features.Experimental outcomes confirm that the ResNet18-based deep features achieve a classification accuracy of 91% with the SoftMax classifier, while the proposed hybrid features provide a classification accuracy of 99.50% with the K-Nearest Neighbor (KNN).These results underscore the significance of the proposed scheme.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".