Covid Chestxray Enhancement Eval

---
name: covid-chestxray-enhancement-eval
description: Evaluates the impact of five image enhancement techniques (histogram equalization, CLAHE, complement, gamma correction, BCET) on six CNN architectures for three-class classification (COVID-19, lung opacity, normal) using chest X-ray images. It also assesses whether lung segmentation improves classification accuracy and model interpretability. Use when the user wants to benchmark on COVQU-20, or asks about evaluating this task. Reports Accuracy.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2012.02238
  bibtex_key: rahman2020exploring
  confidence: high
---

# covid-chestxray-enhancement-eval

> Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-rays Images — Rahman et al. (2020) (arXiv:2012.02238, 2020)

## What this evaluates

Evaluates the impact of five image enhancement techniques (histogram equalization, CLAHE, complement, gamma correction, BCET) on six CNN architectures for three-class classification (COVID-19, lung opacity, normal) using chest X-ray images. It also assesses whether lung segmentation improves classification accuracy and model interpretability.

## Datasets

- **COVQU-20** — total 18479; splits: test (-1); repo https://github.com/IEEE8023/covid-chestxray-dataset

## Metrics

- `Accuracy` **(primary)** — range: percent
  - Percentage of correctly classified images out of the total number of images in the evaluation set.
- `Weighted F1-Score` — range: percent
  - Harmonic mean of precision and recall, weighted by the number of true instances for each class.
- `Weighted Precision` — range: percent
  - Ratio of true positive predictions to all positive predictions, averaged across classes weighted by support.
- `Weighted Recall` — range: percent
  - Ratio of true positive predictions to all actual positive instances, averaged across classes weighted by support.

## Input / output format

**Input**: Chest X-ray images (plain or lung-segmented), optionally pre-processed with one of five enhancement techniques (histogram equalization, CLAHE, image complement, gamma correction, BCET).

**Output**: One of three class labels: 'COVID-19', 'lung opacity', or 'normal'.

## Scoring recipe

```python
def compute_metrics(preds, golds):
    classes = ['COVID-19', 'lung opacity', 'normal']
    accuracy = sum(p == g for p, g in zip(preds, golds)) / len(golds)
    prec, rec, f1 = [], [], []
    for c in classes:
        tp = sum(p == c and g == c for p, g in zip(preds, golds))
        fp = sum(p == c and g != c for p, g in zip(preds, golds))
        fn = sum(p != c and g == c for p, g in zip(preds, golds))
        p = tp / (tp + fp) if (tp + fp) > 0 else 0
        r = tp / (tp + fn) if (tp + fn) > 0 else 0
        f = 2 * p * r / (p + r) if (p + r) > 0 else 0
        prec.append(p); rec.append(r); f1.append(f)
    weighted_f1 = sum(f * support for f, support in zip(f1, [8851, 6012, 3416])) / 18279
    return accuracy * 100, weighted_f1 * 100
```

## Common pitfalls

- The segmentation model's performance on the classification dataset is only qualitatively evaluated because ground truth masks are unavailable for that specific database.
- Deeper architectures (e.g., ResNet101) do not consistently outperform domain-specific models (e.g., CheXNet), highlighting the importance of task-specific pretraining and hyperparameter tuning.
- Image segmentation does not always improve classification accuracy but significantly improves model interpretability by focusing decisions on the lung region of interest.

## Evidence (verbatim from paper)

> Finally, it was seen that the combination of gamma enhancement and ChexNet was the best performing networking for the COVID-19 classification with about 96.29% and 96.28% , accuracy and F1-Score respectively.

## Citation

```bibtex
@misc{rahman2020exploring,
  title={Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-rays Images},
  author={Rahman et al. (2020)},
  year={2020},
  note={arXiv:2012.02238}
}
```

- arXiv: 2012.02238