Tb Classification Eval

---
name: tb-classification-eval
description: Evaluates deep learning models' ability to classify chest X-rays as tuberculosis or normal, comparing whole-image vs. lung-segmented inputs. Use when the user wants to benchmark on Kaggle CXR images and lung mask dataset, or asks about evaluating this task. Reports accuracy.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2007.14895
  bibtex_key: rahman2020reliable
  confidence: high
---

# tb-classification-eval

> Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization — Rahman et al. (2020) (arXiv:2007.14895, 2020)

## What this evaluates

Evaluates deep learning models' ability to classify chest X-rays as tuberculosis or normal, comparing whole-image vs. lung-segmented inputs.

## Datasets

- **Kaggle CXR images and lung mask dataset** — total ?; splits: train (-1), val (-1), test (-1)

## Metrics

- `accuracy` **(primary)** — range: [0, 1]
  - Accuracy = (TP + TN) / (TP + FN + FP + TN). Measures the proportion of correctly classified images.
- `sensitivity` — range: [0, 1]
  - Sensitivity = TP / (TP + FN). Also called recall; measures the proportion of actual TB cases correctly identified.
- `specificity` — range: [0, 1]
  - Specificity = TN / (FP + TN). Measures the proportion of normal cases correctly identified.
- `precision` — range: [0, 1]
  - Precision = TP / (TP + FP). Measures the proportion of predicted TB cases that are actually TB.
- `AUC` — range: [0, 1]
  - Area under the Receiver Operating Characteristic (ROC) curve. Computed by averaging five-fold cross-validation results.
- `F1 score` — range: [0, 1]
  - F1 = (2 * TP) / (2 * TP + FN + FP). Harmonic mean of precision and recall.

## Input / output format

**Input**: Chest X-ray images (either whole image or lung-segmented mask).

**Output**: Binary classification label: TB or normal.

## Scoring recipe

```python
def compute_metrics(y_true, y_pred):
    TP = sum((y_true == 1) & (y_pred == 1))
    TN = sum((y_true == 0) & (y_pred == 0))
    FP = sum((y_true == 0) & (y_pred == 1))
    FN = sum((y_true == 1) & (y_pred == 0))
    accuracy = (TP + TN) / (TP + FN + FP + TN)
    sensitivity = TP / (TP + FN)
    specificity = TN / (FP + TN)
    precision = TP / (TP + FP)
    f1 = (2 * TP) / (2 * TP + FN + FP)
    return {'accuracy': accuracy, 'sensitivity': sensitivity, 'specificity': specificity, 'precision': precision, 'f1': f1}
```

## Common pitfalls

- The paper uses five-fold cross-validation on a relatively small dataset, which can lead to high variance in metrics.
- Segmentation is performed as a separate preprocessing step; errors in lung mask generation may propagate to classification without being accounted for.
- Dataset size is ambiguously stated (704 images for segmentation vs. 4200 for classification in the brief).

## Evidence (verbatim from paper)

> The performance of different CNNs for testing dataset was evaluated after the completion of validation phase and was compared using six performance metrics: accuracy, sensitivity or recall, specificity, precision, area under curve (AUC), F1 score.

## Citation

```bibtex
@misc{rahman2020reliable,
  title={Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization},
  author={Rahman et al. (2020)},
  year={2020},
  note={arXiv:2007.14895}
}
```

- arXiv: 2007.14895