Optimizing Multi Neural Network Weights for COVID-19 Detection Using Enhanced Artificial Ecosystem Algorithm
Bibliographic record
Abstract
The role of machine learning in medical research, particularly in addressing the COVID-19 pandemic, has proven to be significant.The current study delineates the design and refinement of an artificial intelligence (AI) framework tailored to differentiate COVID-19 from Pneumonia utilizing X-ray scans in synergy with textual clinical data.The focal point of this research is the amalgamation of diverse neural networks and the exploration of the impact of metaheuristic algorithms on optimizing these networks' weights.The proposed framework uniquely incorporates a lung segmentation process using a pre-trained ResNet34 model, generating a mask for each lung to mitigate the influence of potential extraneous features.The dataset comprised 579 segmented X-ray images (Anteroposterior and Posteroanterior views) of COVID-19 and Pneumonia patients, supplemented with each patient's textual medical data, including age and gender.An enhancement in accuracy from 94.32% to 97.85% was observed with the implementation of weight optimization in the proposed framework.The efficacy of the model in detecting COVID-19 was further ascertained through a comprehensive comparison with various architectures cited in the existing literature.
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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 | 0.000 | 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 | 0.000 |
| 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".