A Hybrid Dehazing and Illumination Based Approach for Preprocessing, Enhancement and Segmentation of Lung Images Using Deep Learning
Bibliographic record
Abstract
Medical images are affected by various complications such as noise and deficient contrast.To increase the quality of an image, it is highly important to increase the contrast and eliminate noise.In the field of image processing, image enhancement is one of the essential methods for recovering the visual aspects of an image.However, segmentation of the medical images such as brain magnetic resonance imaging (MRI) and lungs computed tomography (CT) scans properly is a difficult task.In this article, a novel hybrid method is proposed for the enhancement and segmentation of lung images.The suggested article includes two steps.In the 1 st step, lung images were enhanced.During enhancement, images were gone through many steps such as de-hazing, complementing, channel stretching, course illumination, and image fusion by principal component analysis (PCA).In the second step, the modified U-Net model was applied to segment the images.We evaluated the entropy of input and output images, peak signal-to-noise ratio (PSNR), gradient magnitude similarity deviation (GMSD), and multi-scale contrast similarity deviation (MCSD) after the enhancement process and compare results with existing adaptive gamma correction with weighted distribution correction (AGCWD) method.During segmentation, we used both original and enhanced images and calculated the Dice-coefficient.We found that the Dicecoefficient was 0.9695 for the original images and 0.9797 for the enhanced images.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".