Enhanced Disease Detection Using Contrast Limited Adaptive Histogram Equalization and Multi-Objective Cuckoo Search in Deep Learning
Bibliographic record
Abstract
Delayed diagnosis of numerous diseases often results in postponed treatment, adversely affecting patient outcomes.By analyzing biological signals and patient photographs, critical information about an individual's health or the severity of a medical condition can be obtained for various diseases.Signals from Electroencephalography (EEG), Electrocardiography (ECG), and Electrooculography (EOG) can be used to predict and diagnose disorders related to the brain, heart, eyes, muscles, and nervous system.Additionally, biomedical images acquired through X-ray, ultrasound, and magnetic resonance imaging can be utilized for disease diagnosis and detection with the help of image processing techniques, artificial intelligence, and deep learning methods.In this study, we propose a novel approach that combines the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm and Multi-Objective Cuckoo Search (MOCS) with Convolutional neural networks (CNNs) to achieve highly accurate disease classification using chest X-ray images.Our method begins by applying a contrast enhancement strategy, specifically, the CLAHE algorithm, with MOCS for optimal parameter selection to attain the highest classification performance.Subsequently, contrast-enhanced images are fed into the CNNs to further improve image quality and classification accuracy.Our approach is employed to categorize three types of chest X-ray images, namely, unhealthy, normal (healthy), and pneumonia.To assess the performance of our proposed method, we utilize the widely-used "COVID-19 Radiography" dataset.Experimental results yield an accuracy rate of 99.16%, a precision rate of 99.20%, and a sensitivity rate of 98.99%.These findings demonstrate that our proposed model outperforms existing techniques in the literature and can be effectively employed for disease detection and classification.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".