Pneumonia Detection AI Case Study
An AI-powered web application for rapid pneumonia diagnosis from chest X-rays, providing instant, reliable results for healthcare professionals.
Project Overview
This AI-powered diagnostic tool helps healthcare professionals quickly identify pneumonia from chest X-ray images. The application uses deep learning models trained on thousands of medical images to provide accurate, instant diagnoses.
The solution addresses the need for faster, more accessible diagnostic tools, especially in areas with limited access to specialized radiologists.
Key Metrics
Challenges
The healthcare problems we needed to solve
Diagnostic Delays
Traditional X-ray analysis requires specialized radiologists, leading to delays in diagnosis and treatment, especially in underserved areas.
Human Error Risk
Manual interpretation of X-rays can be subjective and prone to human error, potentially missing early signs of pneumonia.
Accessibility
Limited access to expert radiologists in remote or resource-constrained healthcare settings creates barriers to timely diagnosis.
Our Solution
How we built the AI diagnostic tool
Deep Learning Model
Developed a convolutional neural network (CNN) trained on a large dataset of chest X-ray images. The model can accurately distinguish between normal lungs and pneumonia cases, with confidence scores and highlighted regions of interest.
- 94%+ accuracy rate
- Sub-5 second processing
- Visual heatmap overlays
AI Features
- • CNN-based image classification
- • Confidence scoring
- • Region of interest highlighting
- • Batch processing support
- • Model versioning
- • Continuous learning capability
Application Features
- • Drag & drop image upload
- • Real-time analysis
- • Detailed diagnostic reports
- • Patient history tracking
- • HIPAA-compliant storage
- • Exportable results
User-Friendly Web Application
Built an intuitive web interface that allows healthcare professionals to upload X-ray images and receive instant diagnostic results. The application includes patient management, result history, and detailed reporting features.
- Simple drag-and-drop interface
- Secure patient data handling
- Comprehensive result reports
Technologies Used
AI/ML
- TensorFlow & Keras
- Python & NumPy
- OpenCV for image processing
- Transfer learning models
Backend
- Python & Flask
- PostgreSQL Database
- RESTful APIs
- Cloud GPU processing
Frontend
- React & Next.js
- TypeScript
- Tailwind CSS
- Image processing libraries
Results & Impact
The measurable outcomes of the project
Diagnostic Accuracy
Average Processing Time
Images Processed
Availability
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