A Shearlet-Based Second Order System for Classifying Oral Cancer: An Analysis of Histopathological Images
Bibliographic record
Abstract
In this study, we propose a Second Order Shearlets (SOS) based system for Oral Cancer Classification (OCC) that leverages histopathological images.The fundamental premise of the system is the observable variations in texture patterns between normal and abnormal cells within these images, which can be exploited for differentiation.The images undergo a transformation from the Red-Green-Blue (RGB) color space to the Hue-Saturation-Value (HSV) color space, followed by the extraction of co-occurrence texture features via the SOS system.Further enhancement of feature extraction is achieved by applying a median filter for de-noising the histopathological images.The proposed SOS-OCC system, equipped with a probabilistic classifier at the final stage, was presented with an assortment of 1224 images for evaluation.The results indicated a noteworthy classification accuracy of 98.6% when employing stratified k-fold cross-validation, thereby underlining the system's efficacy in identifying oral cancer-related abnormalities.Moreover, a comparative analysis was conducted with Wavelet, Curvelet, and Contourlet-based representation systems to underscore the superior performance of the SOS-OCC system.This study provides valuable insights into the application of the SOS approach to oral cancer classification and sets a promising precedent for future 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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".