COVID-19 Diagnosis Using Chaotic Logistic Map Based Modified Whale Optimization: A Robust Feature and Parameter Selection Approach
Bibliographic record
Abstract
The ongoing Coronavirus (COVID-19) pandemic poses a significant global health crisis due to its rapid transmission among humans and animals.Projections suggest that 90% of the world's population could potentially be affected in the years to come, underlining the critical need for early and accurate detection to mitigate the mortality rate.Previous models developed for COVID-19 prediction have primarily relied on manually-extracted features, a process that is time-intensive and prone to human error.In response to this challenge, this study introduces a novel diagnostic tool for COVID-19, leveraging machine learning approaches for efficient feature extraction and optimal parameter selection.Initially, features are extracted from collected Computed Tomography (CT) images using both the Gray Level Co-Occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRM) techniques.Subsequently, a Chaotic Logistic Map-based Modified Whale Optimization (CLM-MWO) algorithm is applied to select the optimal hyperparameters for a Neural Network (NN) classifier and to perform Feature Selection (FS) from the extracted features.The CLM-MWO is inspired by the prey-searching behaviours of whales and incorporates elements of chaos theory and logistic map to enhance exploration and exploitation capabilities.This approach improves the stability and convergence speed of the algorithm, enabling it to identify optimal features and fine-tune the parameters of the NN for superior classification performance.The features extracted through the CLM-MWO are then input into an improved Neural Network (INN) classifier, which facilitates the classification and prediction of COVID-19 from CT images.The proposed CLM-MWO-INN technique is validated through comparison with other classifiers frequently utilized in recent researches.Performance measurements indicate that the proposed method achieves an accuracy of 91.13% and 93.11% on two different CT image datasets.This accuracy surpasses that of other classifiers, demonstrating the potential of the proposed method for effective early detection of COVID-19.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".