Revolutionizing classification: A novel gray level co-occurrence matrix and statistical feature-based segmentation approach
Bibliographic record
Abstract
The accurate and efficient classification of leukemia images is crucial for early diagnosis and effective treatment planning. Traditional methods often face challenges in handling the complexity and variability of medical images. To address these challenges, we propose a novel approach that leverages the Gray Level Co-occurrence Matrix (GLCM) and statistical feature-based segmentation techniques. In this paper, we present a comprehensive framework for the automated classification of leukemia images using advanced image processing techniques. The methodology involves six key stages: input of leukemia images, preprocessing to enhance image quality, segmentation to isolate relevant features, feature extraction using texture analysis, classification using multiple distance metrics Euclidean, Manhattan, Canberra, and Chebyshev, and performance evaluation. Our results demonstrate significant improvements in classification accuracy, sensitivity, specificity, and error rates across various metrics and feature sets. For instance, using the Chebyshev distance, we achieved an average accuracy of 82.69%, sensitivity of 85.95%, and specificity of 82.77%. The Canberra distance provided optimal performance with 65 features, yielding an accuracy of 85.18%, sensitivity of 86.39%, and specificity of 86.31%. These findings underscore the efficacy of our approach in distinguishing between healthy and leukemic cells, thereby contributing to early diagnosis and effective treatment planning for leukemia.
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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 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".