An Automated Computed Tomography Scan Analysis Framework for COVID-19 Detection Using Machine Learning
Bibliographic record
Abstract
During the coronavirus disease-19 (COVID-19) epidemic, there has been a growing need for rapid diagnostic tools, with Computed Tomography (CT) scans emerging as essential diagnostic resources.Nevertheless, the process of manually interpreting their findings, although informative, is nevertheless characterized by a significant amount of work and variability.In the current study, we intend to construct a machine learning-based model to automate the evaluation of CT images for COVID-19 diagnosis and to differentiate it from pneumonia and other non-COVID diseases.The model we propose employs a Tolerant Local Median Fuzzy C-means (TLMFCM) segmentation strategy in conjunction with the Stacked Sparse Autoencoder (SSAE) for robust feature extraction.The classification task employs a Locally Controlled Seagull Kernel Extreme Machine Learning (LCS-KELM) whose parameters are optimized with the Seagull Optimization algorithm (SOA).Our model performed better than other models in preliminary comparisons against traditional benchmarks, with an accuracy of 96.3% and a faster processing time.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".