🎓 Research Team
e19366@eng.pdn.ac.lk
e19224@eng.pdn.ac.lk
e18059@eng.pdn.ac.lk
👨🏫 Supervisors
📋 Abstract
Lung nodules, potential early signs of lung cancer, are typically detected via CT scans, but their high cost and radiation exposure limit frequent use. Chest X-rays (CXRs) are more accessible but often miss small nodules, leading to delayed diagnoses.
This project develops an AI-powered system for detecting and tracking lung nodules using CXRs, reducing reliance on CT scans. It employs deep learning-based Computer-Aided Detection (CAD) techniques, utilizing Digitally Reconstructed Radiographs (DRRs) to train the AI model for enhanced detection and follow-up.
By integrating feature extraction, image registration, and deep learning, the system offers a reliable, automated solution for lung nodule monitoring. Validated against real-world datasets and radiologist interpretations, the AI system aims to improve early detection and tracking, aiding clinical decisions while minimizing unnecessary CT referrals.
🔬 Related Works
Challenge Identified
Early studies highlighted CXR limitations in detecting small nodules
AI Advancement
Deep learning (CNNs, YOLOv4) significantly advanced automated detection
Clinical Integration
AI models integrated into hospital PACS for real-time detection
Global Impact
Making screening accessible in resource-limited settings
⚙️ Methodology
Proposed Workflow
Anonymize CT scans with nodules and corresponding CXRs using advanced privacy protection techniques.
Convert CT volumes to Digitally Reconstructed Radiograph (DRR) images that mimic real X-rays.
Map annotated nodules from CT scans to DRRs for supervised learning and ground truth establishment.
Train deep learning models (CNNs, YOLO) to detect and localize nodules with high accuracy.
Align CXRs over time using image registration to detect changes in nodule size and position.
Generate millimeter-scale, image-annotated reports for radiologists with clinical insights.
💻 Implementation Details
🔧 Technology Stack
🖥️ Infrastructure
Cloud Computing: Google Colab Pro/Pro+, AWS EC2 with NVIDIA T4/A100 GPUs for high-performance training and inference.
Local Development: Machines with ≥16GB RAM and NVIDIA GTX 1660+ for development and testing.
📊 Datasets
CT Data: LIDC-IDRI, NLST datasets for comprehensive nodule annotations and ground truth.
CXR Data: CheXpert, JSRT, VinDr-CXR datasets for diverse chest X-ray training data.
Preprocessing: Images standardized to 512x512 pixels with DICOM anonymization using advanced deid techniques.
🎯 Predicted Outcomes
Enhanced Detection
Significantly reduced false negatives in nodule detection
Feature Optimization
Advanced feature extraction to assist radiologists
Clinical Support
Reliable AI system supporting radiologist decision-making
Cost Reduction
Lower follow-up costs through efficient screening
Safer Screening
Reduced radiation exposure for patients
Global Access
Improved accessibility in resource-limited settings
📝 Conclusion
Our research develops a groundbreaking AI-powered system for lung nodule detection and follow-up using chest X-rays, significantly enhancing accessibility while reducing reliance on expensive CT scans. By leveraging cutting-edge deep learning techniques and innovative DRR-based training methodologies, our system represents a major advancement in medical AI.
The system improves early detection accuracy, automates complex nodule tracking processes, and provides invaluable support to radiologists in clinical decision-making. This research contributes to making life-saving lung cancer screening more accessible, efficient, and cost-effective worldwide.
📚 Publications
This project has not yet resulted in any publications