🫁 PulmoScan: AI-Powered Lung Cancer Detection and Classification

Welcome to PulmoScan, a comprehensive deep learning project for automated detection and classification of pulmonary nodules from CT scans using 3D Convolutional Neural Networks and radiomics-based machine learning. This work implements the approach described in:

"3D Deep Learning from CT Scans Predicts Tumor Invasiveness of Subcentimeter Pulmonary Adenocarcinomas" Cancer Research, 2018 (arXiv:1801.09555)

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🚀 Project Overview

PulmoScan offers a modular pipeline to detect, classify, and analyze pulmonary nodules using state-of-the-art 3D deep learning architectures and advanced radiomic feature extraction. The system processes CT scans through carefully tuned preprocessing pipelines, trains multi-stage detection and classification models, and validates improvements using comprehensive clinical metrics.

📋 Key Features

• Advanced CT Preprocessing: Lung segmentation with Sobel edge detection, Otsu thresholding, and morphological operations

• 3D Dual Path Network (DPN) Architecture: Implements attention-enhanced 3D CNN with multi-scale feature extraction for nodule detection

• Radiomics-Enhanced Classification: PyRadiomics-based feature extraction with Random Forest, SVC, and KNN models for malignancy prediction

• Multi-Dataset Support: Works with LUNA16, LIDC-IDRI, and Chest CT-Scan datasets with standardized processing

• VGG19-based Subtype Classification: Validates cancer subtypes with transfer learning achieving 92% accuracy

• Clinical-Grade Benchmarking: Comprehensive metrics including AUC, sensitivity, specificity, and F1 scores

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🏗️ Project Structure

📁 pulmoscan/
├── 📁 data/                      # Raw datasets
│   ├── 📁 luna16/
│   ├── 📁 lidc-idri/
│   └── 📁 chest-ct/
├── 📁 processed/                 # Preprocessed outputs
│   ├── 📁 segmented/
│   ├── 📁 normalized/
│   └── *.csv                     # Metadata and annotations
├── 📁 models/                    # Saved models
│   ├── 📁 nodule_detector/       # 3D DPN models
│   ├── 📁 malignancy_classifier/ # RF, SVC, KNN models
│   └── 📁 subtype_classifier/    # VGG19 models
├── 📁 experiments/               # Experimental logs and config
├── 📁 app/                       # Flask application
│   ├── 📁 static/
│   ├── 📁 templates/
│   ├── 📁 utils/
│   │   ├── preprocessing.py      # Segmentation and normalization
│   │   ├── feature_extraction.py # Radiomics and semantic features
│   │   └── visualization.py      # Result visualization
│   ├── routes.py
│   └── __init__.py
├── 📁 monitoring/                # Prometheus & Grafana
│   ├── 📁 prometheus/
│   └── 📁 grafana/
├── 📁 tests/                     # Unit and integration tests
├── requirements.txt
├── Dockerfile
├── docker-compose.yml
└── main.py                       # Pipeline launcher

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Detailed Architecture

Complete Pipeline Workflow

Our implementation follows a four-stage clinical pipeline:

1. Data Preparation: CT scan loading, lung segmentation, Hounsfield normalization, and 3D resampling
2. Nodule Detection: 3D DPN architecture with multi-scale feature extraction and region proposal
3. Malignancy Classification: Radiomic feature extraction followed by ensemble ML classification
4. Subtype Identification: Transfer learning with VGG19 for cancer subtype prediction

📐 Key Hyperparameters

Parameter	Value	Purpose
BATCH_SIZE	16	Optimized for GPU memory with 3D volumes
LEARNING_RATE	0.0001	Fine-tuned for stable convergence in 3D CNNs
INPUT_SIZE	64×64×64	Voxel dimensions for nodule patches
HU_MIN / HU_MAX	-1000 / 400	Hounsfield unit clipping for lung tissue
AUGMENTATION	Rotation, Flip, Elastic	Data augmentation for robust generalization
NUM_FEATURES	107	PyRadiomics features (first-order, shape, texture)
ENSEMBLE_MODELS	RF, SVC, KNN	Multi-model voting for classification
VGG19_LAYERS	19	Deep feature extraction with transfer learning

3D Dual Path Network (DPN)

Our nodule detection implementation uses a 3D DPN architecture specifically optimized for CT volumes:

• Encoder: Multi-scale 3D convolutional layers with residual connections
• Dual Path Structure: Combines high-resolution and high-level semantic features
• Decoder: 3D transposed convolutions with skip connections
• Training: Weighted cross-entropy loss with AdamW optimizer and ReduceLROnPlateau scheduler

This approach achieves AUC ≈ 0.91 for nodule detection, outperforming traditional 2D approaches.

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Dataset Processing

All CT scans are processed with clinical-grade precision using carefully selected parameters:

Preprocessing Pipeline

• Lung Segmentation: Sobel edge detection + Otsu thresholding + morphological operations
• HU Normalization: Clipping to [-1000, 400] and scaling to [0, 1]
• Resampling: Standardized voxel spacing of 1×1×1 mm³
• Patch Extraction: 64×64×64 voxel patches centered on nodule candidates
• Augmentation: 3D rotation, flipping, and elastic deformation

Dataset Details

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Dataset	Scans	Annotations	Task	Notes
LUNA16	888 CT scans	1,186 nodules	Detection	Grand Challenge for nodule detection
LIDC-IDRI	1,018 scans	~2,600 nodules	Classification	4 radiologist annotations per nodule
Chest CT-Scan	1,000 images	4 classes	Subtyping	Adenocarcinoma, Squamous, Large cell, Normal

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Nodule Detection: 3D DPN Implementation

Our 3D DPN implementation incorporates several architectural innovations:

• Multi-Scale Feature Fusion: Combines features from multiple resolution levels
• Residual Connections: Prevents gradient vanishing in deep networks
• Weighted Loss Function: Addresses class imbalance (nodule vs. non-nodule)
• Early Stopping & LR Scheduling: Ensures optimal convergence without overfitting

Detection Performance

Metric	Value	Clinical Significance
AUC-ROC	0.91	Excellent discrimination capability
Sensitivity	87.3%	High true positive rate
Specificity	89.6%	Low false positive rate
Precision	0.88	Reliable positive predictions
F1 Score	0.875	Balanced performance

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Malignancy Classification: Radiomics + ML

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The radiomics-based classification represents our interpretable ML approach:

• Feature Extraction: 107 PyRadiomics features (first-order statistics, shape, texture)
• Semantic Features: XML-based annotations from LIDC-IDRI using PyLIDC
• Feature Selection: Recursive feature elimination with cross-validation
• Ensemble Classification: Random Forest, SVC, and KNN with voting strategy
• Validation: 5-fold stratified cross-validation for robust evaluation

Radiomics vs. Deep Learning Comparison

Model	Accuracy	F1 Score	Notable Strength
Random Forest	91.2%	0.90	Interpretable features & fast inference
SVC	88.7%	0.87	Strong generalization with RBF kernel
KNN	85.3%	0.84	Simple baseline with good performance
3D CNN (End-to-end)	89.8%	0.89	Automated feature learning

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Cancer Subtype Classification

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Our subtype classification model identifies four key lung tissue categories:

VGG19 Transfer Learning Results

Subtype	Precision	Recall	F1 Score	Support
Adenocarcinoma	0.94	0.91	0.92	250
Squamous Cell	0.91	0.93	0.92	250
Large Cell	0.89	0.90	0.89	250
Normal	0.95	0.94	0.94	250
Overall	0.92	0.92	0.92	1000

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Clinical Validation Results

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The integrated pipeline demonstrates significant clinical value:

Nodule Detection Performance (LUNA16)

Metric	Baseline	PulmoScan	Improvement
Detection Rate	82.4%	91.3%	+8.9%
False Positives	4.2/scan	2.1/scan	-50%
AUC	0.87	0.91	+4.6%

Malignancy Classification (LIDC-IDRI)

Approach	Accuracy	Sensitivity	Specificity	F1 Score
Radiologist Average	88.5%	84.2%	91.3%	0.87
PulmoScan (RF)	91.2%	89.7%	92.8%	0.90
Improvement	+2.7%	+5.5%	+1.5%	+0.03

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🛠 Setup Instructions

# 1. Clone Repository
git clone https://github.com/dhouhameliane/PulmoScan
cd pulmoscan

# 2. Create Virtual Environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# 3. Install Dependencies
pip install -r requirements.txt

# 4. Download Datasets
# LUNA16: https://luna16.grand-challenge.org/
# LIDC-IDRI: https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI
# Place datasets in data/ directory

# 5. Run Preprocessing
python main.py --mode preprocess --dataset luna16

# 6. Train Models
python main.py --mode train --model nodule_detector

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📡 API Reference

Core Endpoints

# Upload CT scan for analysis
POST /upload
Content-Type: multipart/form-data
Body: {file: CT_scan.mhd}

# Get prediction results
GET /report/<scan_id>
Response: {
  "nodules_detected": int,
  "malignancy_scores": [float],
  "subtypes": [string],
  "confidence": float
}

# Visualize 3D results
GET /visualize/<scan_id>
Response: Interactive 3D visualization

# Download diagnostic report
GET /download/<scan_id>
Response: PDF report with findings

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🧪 Running Tests

# Run all tests
pytest tests/

# Run specific test suites
pytest tests/test_preprocessing.py -v
pytest tests/test_models.py -v
pytest tests/test_api.py -v

# Generate coverage report
pytest --cov=app tests/

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📊 Monitoring & Observability

PulmoScan includes comprehensive monitoring:

• Prometheus Metrics: Inference time, model accuracy, error rates
• Grafana Dashboards: Real-time visualization of system health
• Custom Metrics: Per-model performance tracking
• Alerting: Automated notifications for anomalies

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🔬 Technical Implementation Details

Preprocessing Pipeline

# Key preprocessing steps
1. Load DICOM/MHD files
2. Resample to 1×1×1 mm³ spacing
3. Apply lung segmentation mask
4. Clip HU values to [-1000, 400]
5. Normalize to [0, 1]
6. Extract 64×64×64 patches
7. Apply data augmentation

Feature Extraction

# Radiomic features (107 total)
- First Order (19): Mean, Median, Std, Skewness, Kurtosis, etc.
- Shape (14): Volume, Surface Area, Sphericity, etc.
- Texture (74): GLCM, GLRLM, GLSZM, NGTDM, GLDM

Model Training

# Training configuration
- Optimizer: AdamW (weight_decay=1e-4)
- Loss: Weighted CrossEntropy
- LR Schedule: ReduceLROnPlateau (patience=5)
- Early Stopping: patience=10
- Batch Size: 16
- Epochs: 100 (with early stopping)

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🚀 Future Improvements

• Integrate explainability with Grad-CAM and attention visualization
• Add multi-task learning for simultaneous detection and classification
• Enhance with transformer-based architectures (3D Vision Transformers)
• Develop real-time inference pipeline for clinical deployment
• Expand to multi-center validation with external datasets
• Implement uncertainty quantification for model predictions
• Web interface with PACS integration for clinical workflow

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👥 Authors

Project Team (ESPRIT Data Science, 2024-2025):

• Asser Aydi - Lead Developer
• Dhouha Meliane - ML Engineer & Architecture
• Harold Agbervo - Data Preprocessing
• Nouha Aouachri - Model Evaluation
• Ranim Souissi - Web Development

Supervisors:

• Ms. Sarah Zouari - Academic Supervisor
• Mr. Fares Khfecha - Technical Advisor

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📜 License

Licensed under MIT License.
Created by ESPRIT Data Science Team
📧 Contact: dhouhameliane@esprit.tn

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📬 References

Scientific Papers

• Wang, S., et al. "3D Deep Learning from CT Scans Predicts Tumor Invasiveness of Subcentimeter Pulmonary Adenocarcinomas" arXiv:1801.09555
• Setio, A. A., et al. "Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge" Medical Image Analysis, 2017
• Armato III, S. G., et al. "The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI)" Medical Physics, 2011

Datasets

• LUNA16 Grand Challenge
• LIDC-IDRI (The Cancer Imaging Archive)
• Chest CT-Scan Images Dataset (Kaggle)

Tools & Libraries

• PyRadiomics Documentation
• PyLIDC GitHub Repository
• VGG19 Architecture (Keras)

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🙏 Acknowledgments

We thank ESPRIT for providing computational resources and the open-source community for maintaining the datasets and tools that made this project possible. Special thanks to the radiologists who contributed annotations to LIDC-IDRI and the LUNA16 challenge organizers.

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