An end-to-end machine learning pipeline for tuberculosis screening using cough audio analysis, designed for resource-limited clinical settings.

This project implements a machine learning pipeline for TB screening using:

• CNN: Mel-spectrogram feature extraction with focal loss
• XGBoost: Ensemble learning on embeddings + clinical data
• Data Augmentation: ESC-50 environmental noise for robustness
• Imbalanced Learning: Handles 30% TB+, 70% TB- distribution

graph TD
    A[Mel-Spectrograms] --> B[CNN Encoder]
    B --> C[Embeddings 256D]
    D[Clinical Features] --> E[XGBoost Classifier]
    C --> E
    E --> F[Logistic Regression Stacker]
    F --> G[Final TB Prediction]

• Clean: 0.862
• Gaussian: 0.855
• Environmental: 0.833

Patient-wise split; results computed on a held-out validation set consistent with the notebook.

1. Clone repository: git clone https://github.com/your-username/AcousticTB.git cd AcousticTB

2. Install dependencies: pip install tensorflow scikit-learn xgboost librosa matplotlib seaborn tqdm pandas numpy

3. Run data augmentation: python %run src/data_augmentation.py

4. Execute pipeline: Open and run \AcousticTB_FINAL.ipynb\

AcousticTB/
├── AcousticTB_FINAL.ipynb     # Main pipeline notebook
├── data/
│   ├── raw/                      # Original CODA dataset (available at https://www.synapse.org/Synapse:syn31472953/wiki/617828) 
│   │   └── solicited_coughs/
│   │       ├── audio/            # .wav files
│   │       └── metadata/         # Clinical data
│   └── processed/                # Mel-spectrograms & metadata
│       └── features/             # Processed .npy files
├── src/
│   ├── data_augmentation.py      # Noise augmentation pipeline
│   └── utils.py                  # Helper functions
├── requirements.txt           # Dependencies
└── README.md                  # This file

CNN Encoder (256 parameters: 1.2M)

• Conv2D(64) → BatchNorm → Conv2D(64) → MaxPool → Dropout(0.2)
• Conv2D(128) → BatchNorm → Conv2D(128) → MaxPool → Dropout(0.3)
• Conv2D(256) → BatchNorm → Conv2D(256) → MaxPool → Dropout(0.4)
• GlobalAvgPool → Dense(512) → Dense(256) → Dense(1, sigmoid)

• focal_loss = -α * (1-p_t)^γ * log(p_t)
• γ = 2.0: Strong focus on hard examples
• α = 0.25: Moderate class balancing
• Optimized for AUROC in a medical context

• Clean cough sounds: Original recordings
• Environmental noise: ESC-50 real-world sounds (rain, traffic, etc.)
• Gaussian noise: Statistical backup augmentation
• SNR=20dB: Realistic noise levels

1. CNN: Extracts 256-dim embeddings from mel-spectrograms
2. XGBoost: Combines embeddings + clinical features
3. Logistic Regression: Final stacking layer

learning_rate: 0.0751 n_estimators: 661 max_depth: 7 subsample: 0.8320 colsample_bytree: 0.8448 reg_alpha: 0.0223 reg_lambda: 0.2689 scale_pos_weight: 6.1896 objective: binary:logistic, eval_metric: auc, tree_method: hist

solver: lbfgs max_iter: 2000 regularization: L2 (C = 0.1) class_weight: balanced 5-fold CV TB-composite score ≈ 0.888 (0.56·Sensitivity + 0.44·Specificity)

Designed for resource-limited clinical settings where:

• TB screening is critical, but diagnostic tools are limited
• High sensitivity required (avoid missing TB+ cases)
• Acceptable specificity to minimize false alarms
• Robust to environmental noise conditions

TB cough audio: CODA TB DREAM dataset (restricted; request access on [Synapse]: https://www.synapse.org/Synapse:syn31472953/wiki/619711 Environmental sounds: ESC-50 https://github.com/karolpiczak/ESC-50?tab=readme-ov-file#citing Processing: 64×64 log-Mel spectrograms (librosa STFT + Mel filters; typical fs ≈ 22 kHz)

The model includes a comprehensive evaluation:

• ROC/PR curves: Performance visualization
• Confusion matrices: Clinical decision analysis
• Feature importance: Interpretability for medical use
• Noise resilience: Performance across environments

Results on validation set: Total samples: 5865 | TB+: 1785 (30.4%) | TB-: 4080 (69.6%) Training patients: 7817 | Validation patients: 1955

• Python 3.8+
• TensorFlow 2.8+
• XGBoost 1.6+
• Librosa 0.9+
• Scikit-learn 1.0+

• TB Cough Data: CODA TB Challenge dataset (restricted access)
• Environmental Sounds: ESC-50 (publicly available)
• Processing: Mel-spectrograms (64x64) at 22kHz

Advanced ensemble architecture combining deep learning and gradient boosting with focal loss optimization specifically tuned for medical screening scenarios where missing positive TB cases have significantly higher clinical cost than false positives.