A deep learning-based technique for identifying COVID-19 from chest X-ray images
Why this work is in the frame
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Bibliographic record
Abstract
This study uses deep learning algorithms and CT (Computed Tomography) scans to diagnose COVID-19.First, we introduce a novel method to reduce noise in CT images by combining wavelet transformation with fuzzy logic.Then, using the suggested combined global and local threshold technique, we segmented lung pictures.Lung areas from CT scans can be successfully segregated in this manner.Features and categorization will be extracted in the following stage.While an SVM (Support Vector Machine) is used for classification, AlexNet extracts features.Three categories of data are categorized with a 99.8% accuracy: COVID-19, Viral Pneumonia, and Normal.The proposed strategy outperforms earlier approaches in terms of classification performance.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it