Advanced COVID-19 CT Image Segmentation Using a Hybrid Undecimated Wavelet Transform, Fuzzy Clustering, and Anisotropic Diffusion Approach
Bibliographic record
Abstract
Early detection of Coronavirus Disease 2019 , an infectious disease caused by the SARS-CoV-2 virus, is crucial in minimizing the risk of mortality and limiting its spread, particularly among asymptomatic individuals.Computed tomography (CT) scans of the chest are commonly employed for diagnosing this condition, necessitating the development of segmentation techniques for analyzing these images effectively.However, segmenting COVID-19 CT images poses considerable challenges due to the indistinct boundaries between gray and white matter, as well as the homogeneous and ambiguous structures within the regions.To address these issues, we propose a hybrid approach that combines Undecimated Wavelet Transform (UWT), Fuzzy Clustering (FC), and Anisotropic Diffusion Filter (ADF).Our method involves utilizing UWT to denoise CT images in the frequency domain, followed by an advanced fuzzy clustering technique based on texture features and local gray value entropy for autonomous segmentation of CT images.The segmented images are then processed with ADF to eliminate uncertainty and noise.The performance of our proposed method was evaluated visually and through similarity measurements using an open-source dataset.A comparative analysis with alternative segmentation methods was conducted using multiple metrics, including Dice, Jaccard, Precision, Accuracy, Sensitivity, F-measure, MCC, and Specificity.Our results demonstrate that the proposed hybrid approach significantly enhances the detection of COVID-19 from CT images.
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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".