AI-Powered Pneumonia Detection System
ML & AIDeep learning chest X-ray analysis system with automated risk assessment and PDF reporting (85.58% accuracy using ResNet-18).
Overview
💡 Challenge
Healthcare providers need rapid, accurate pneumonia screening from chest X-rays, but manual analysis is time-consuming and requires specialist expertise. Patients also need comprehensive risk assessment based on clinical symptoms.
⚡ Solution
Developed an AI-powered diagnostic system using PyTorch ResNet-18 CNN for chest X-ray classification, integrated with clinical risk scoring algorithm and automated PDF report generation with visualizations.
🎯 Impact
Provides instant pneumonia detection with 85.58% accuracy, comprehensive patient risk assessment, and professional medical reports - enabling faster clinical decision-making and improved patient care workflows.
Technical Details
🛠️ Tech Stack
✨ Key Features
- ✓ResNet-18 CNN trained for binary classification (Normal vs Pneumonia)
- ✓Real-time chest X-ray analysis with confidence scoring
- ✓Clinical risk assessment algorithm based on patient symptoms and medical history
- ✓Automated PDF report generation with colorful charts and visualizations
- ✓Interactive patient data entry with comprehensive symptom tracking
- ✓Cloud-deployed model hosting using Google Drive integration
- ✓Beautiful medical-grade UI with professional design
Key Learnings
- →Deep learning model deployment requires careful handling of large model files (used Google Drive hosting)
- →Medical AI applications need explainable predictions and confidence scores for clinical trust
- →Combining ML predictions with rule-based clinical assessment provides comprehensive patient evaluation
- →UI/UX design for healthcare applications requires clarity and professional medical aesthetics
- →Python 3.13 compatibility issues with PyTorch required version management during deployment
📊 Data Notes
This project uses synthetic/open data to demonstrate capabilities while maintaining privacy and confidentiality. All methods and approaches are applicable to real-world scenarios.