Computer-aided diagnosis for prostate cancer using support vector machine
Bibliographic record
Abstract
The work in this paper aims for analyzing texture features of the prostate using Trans-Rectal Ultra-Sound images (TRUS) images for tissue characterization. This research is expected to assist beginner radiologists with the decision making. Moreover it will also assist in determining the biopsy locations. Texture feature analysis is composed of four stages. The first stage is automatically identifying Regions Of Interest (ROI), a step that was usually done either by an expert radiologist or by dividing the whole image into smaller squares that represent regions of interest. The second stage is extracting the statistical features from the identified ROIs. Two different statistical feature sets were used in this study; the first is Grey Level Dependence Matrix features. The second feature set is Grey level difference vector features. These constructed features are then ranked using Mutual Information (MI) feature selection algorithm that maximizes MI between feature and class. The obtained feature sets, the combined feature set as well as the reduced feature subset were examined using Support Vector Machine (SVM) classifier, a well established classifier that is suitable for noisy data such as those obtained from the ultrasound images. The obtained sensitivity is 83.3%, specificity ranges from 90% to 100% and accuracy ranges from 87.5% to 93.75%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".