Comparative Study of LBP and HOG Feature Extraction Techniques for COVID-19 Pneumonia Classification
Bibliographic record
Abstract
In this research, we explore the potential of combining effective feature extraction techniques with traditional machine-learning algorithms to classify different types of pneumonia from chest X-ray images. The accurate identification of COVID-19 pneumonia, as well as differentiating it from normal X-rays and other viral pneumonia cases, is crucial in supporting physicians with efficient and reliable diagnoses during times of heavy pressure on the medical system. We present a machine learning-based model for classifying COVID-19 pneumonia-affected lungs, non-COVID pneumonia-affected lungs, and healthy lungs based on chest X-ray images. Specifically, we employ a local binary pattern (LBP) feature extraction technique in conjunction with a support vector machine (SVM) model to achieve 100% accuracy in distinguishing COVID-19 pneumonia from normal X-ray images, which outperforms state-of-the-art methods. In the multiclass classification task, we utilize a Histogram of Oriented Gradients (HOG) feature extraction technique with an SVM-based model, attaining an accuracy of 94%. These results are highly competitive with the performance of deep learning methods commonly used in this domain.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".