An innovative iOS application that brings the power of machine learning to X-ray image analysis. Built with Swift and leveraging Apple's CoreML framework, this app demonstrates the practical application of AI in healthcare technology.
Technical Implementation
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Core Technologies:
- Swift 6.0.2
- CoreML for machine learning integration
- Vision framework for image analysis
- SwiftUI for user interface
- Core Image for image processing
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Machine Learning Features:
- Custom-trained ML model for X-ray analysis
- Real-time image classification
- Abnormality detection and highlighting
- Confidence score calculation
Key Features
Image Processing
- Support for multiple X-ray image formats
- Image preprocessing and enhancement
- Real-time image analysis
- Zoom and pan capabilities for detailed examination
Analysis Capabilities
- Detection of common abnormalities
- Classification of X-ray types
- Detailed analysis reports
- Historical data tracking
User Experience
- Intuitive interface for medical professionals
- Quick scan mode for rapid analysis
- Detailed view mode for thorough examination
- Export and share functionality
Technical Challenges
One of the main challenges was optimizing the CoreML model for mobile devices while maintaining high accuracy. We implemented:
- Model quantization for reduced size
- Batch processing for improved performance
- Memory management optimizations
- Background processing for large images
Impact
The application demonstrates the potential of mobile devices in medical diagnosis support, providing:
- Faster preliminary analysis
- Increased accuracy in detection
- Improved accessibility to X-ray analysis tools
- Support for medical professionals in remote locations
Future Development
Planned enhancements include:
- Support for more types of medical imaging
- Integration with hospital information systems
- Enhanced ML model with broader detection capabilities
- Cloud synchronization for medical records