Detection of Skin Cancer Image by Feature Selection Methods Using New Buzzard Optimization (BUZO) Algorithm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Feature selection is used in machine learning as well as in statistical pattern recognition. This is important in many applications, such as classification. There are so many extracted features in these applications which are either useless or do not have much information. If not removing these features, make raises the computational burden for the main application. In different methods of feature selection, a subset is selected as the answer, which can optimize the value of an evaluation function. In this study, a new algorithm for classification of Dermoscopy images into two types of malignant and benign are presented. To develop the general skin cancer detection system, at first a pre-processing step is applied to enhance image quality. Then the lesion area is removed from the healthy areas using the Otsu threshold method. Nine shape feature and nine color features are extracted from the segmented image using different optimization schema. At the end of the operation, classification was done by SVM, KNN and Decision Tree methods. The results show that combination of buzzard optimization algorithm for feature extraction and SVM classifier accuracy is 94.3%. This result shows the high potential of buzzard optimization algorithm for feature extraction.
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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 | 0.001 |
| 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 it