Dual Deep Learning and Feature-Based Models for Classification of Laryngeal Squamous Cell Carcinoma Using Narrow Band Imaging
Bibliographic record
Abstract
Laryngeal Squamous Cell Carcinoma (LSCC) is a prevalent form of laryngeal cancer that originates from the mucosal surface of the larynx.The visual analysis of laryngeal tissue vascular patterns poses a significant challenge, as it heavily relies on the expertise and experience of medical practitioners.This paper proposes a dual approach for the early diagnosis of LSCC by employing a lightweight Deep Convolutional Neural Network (CNN) and statistical features.It further delves into feature visualization and interpretation of the proposed classification models.Methods: The initial step involves enhancing image quality through Contrast Limited Adaptive Histogram Equalization (CLAHE).In the first approach, we employ a modified SqueezeNet for classifying laryngeal tissues.In the second approach, we extract a combination of first-order statistical features -Percentile-25, Percentile-50, Percentile-75, Mean, and Standard Deviation of each RGB channeland second-order statistical features such as Contrast, Energy, Homogeneity, and Correlation from the Gray-Level Co-Occurrence Matrix (GLCM).These features are then classified using the Extreme Gradient Boosting (XGBoost) classification model.Results: The proposed models are trained and validated using an augmented publicly available dataset, prepared for both binary and multiclass classifications.The results indicate that the proposed models demonstrate exceptional accuracy and efficiency in classifying types of laryngeal cancer.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".