# Ecovnet Covidx Eval

> ecovnet-covidx-eval

- Skill: `qhjqhj00/ecovnet-covidx-eval` (Agent Skill)
- Install (CLI): `npx skillmds@latest add qhjqhj00/ecovnet-covidx-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/ecovnet-covidx-eval/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/qhjqhj00/ecovnet-covidx-eval

---


# ecovnet-covidx-eval

> ECOVNet: An Ensemble of Deep Convolutional Neural Networks Based on EfficientNet to Detect COVID-19 From Chest X-rays — Chowdhury et al. (2020) (arXiv:2009.11850, 2020)

## What this evaluates

Evaluates the ability of deep convolutional neural networks to classify chest X-ray images into three categories: COVID-19, normal, and pneumonia. It probes robustness under class imbalance and tests the effectiveness of ensemble learning strategies (hard vs. soft voting) combined with data augmentation.

## Datasets

- **COVIDx** — total ?; splits: train (12525), validation (1389), test_balanced (300), test_imbalanced (1579)

## Metrics

- `accuracy` **(primary)** — range: [0, 1]
  - Calculated as (TP + TN) / Total Samples. Represents the proportion of correctly classified instances out of all instances.
- `precision` — range: [0, 1]
  - Calculated as TP / (TP + FP). Measures the proportion of positive predictions that are actually correct.
- `recall` — range: [0, 1]
  - Calculated as TP / (TP + FN). Measures the proportion of actual positives that are correctly identified.
- `F1 score` — range: [0, 1]
  - Calculated as 2 * (Precision * Recall) / (Precision + Recall). The harmonic mean of precision and recall, preferred for imbalanced datasets.
- `AUC` — range: [0, 1]
  - Area under the Receiver Operating Characteristic (ROC) curve, which plots True Positive Rate (Recall) against False Positive Rate (FP / (FP + TN)) across different thresholds.

## Input / output format

**Input**: Single chest X-ray image, optionally augmented, resized to model-specific resolution (224x224 to 456x456 depending on EfficientNet variant).

**Output**: Class label prediction (COVID-19, Normal, or Pneumonia) or class-wise probability scores for ensemble aggregation.

## Scoring recipe

```python
def compute_metrics(y_true, y_pred, y_prob=None):
    tp = sum(1 for t, p in zip(y_true, y_pred) if t == p == 1)
    tn = sum(1 for t, p in zip(y_true, y_pred) if t == p == 0)
    fp = sum(1 for t, p in zip(y_true, y_pred) if t == 0 and p == 1)
    fn = sum(1 for t, p in zip(y_true, y_pred) if t == 1 and p == 0)
    acc = (tp + tn) / (tp + tn + fp + fn)
    prec = tp / (tp + fp) if (tp + fp) > 0 else 0
    rec = tp / (tp + fn) if (tp + fn) > 0 else 0
    f1 = 2 * prec * rec / (prec + rec) if (prec + rec) > 0 else 0
    auc = roc_auc_score(y_true, y_prob) if y_prob is not None else None
    return acc, prec, rec, f1, auc
```

## Common pitfalls

- The natural test set is highly imbalanced; the paper explicitly constructs a separate 'balanced' test set (100 per class), so reporting results without specifying which test split was used is misleading.
- Soft ensemble averages softmax probabilities while hard ensemble uses majority voting; confusing the two reverses their performance ranking on certain splits.
- Confidence intervals are computed per test set; the balanced test set contains only 300 samples, yielding significantly wider CIs than the imbalanced set, which can be misinterpreted as lower model stability.

## Evidence (verbatim from paper)

> In order to evaluate the performance of the proposed method, we considered the following evaluation metrics: accuracy, precision, recall, F1 score, confidence interval (CI), receiver operating characteristic (ROC) curve and area under the curve (AUC). The definitions of accuracy, precision, recall and F1 score are as follows: Accuracy = (TP+TN)/Total Samples, Precision = TP/(TP+FP), Recall = TP/(TP+FN), F1 = 2*(Precision*Recall)/(Precision+Recall). Since the benchmark data set is not balanced, F1 score may be a more substantial evaluation metric.

## Citation

```bibtex
@misc{chowdhury2020ecovnet,
  title={ECOVNet: An Ensemble of Deep Convolutional Neural Networks Based on EfficientNet to Detect COVID-19 From Chest X-rays},
  author={Chowdhury et al. (2020)},
  year={2020},
  note={arXiv:2009.11850}
}
```

- arXiv: 2009.11850