Edge-Deployable Deep Segmentation of Breast Ultrasound Images via Optimized U-Net
Research by Javad Ibrahimli presenting an optimized U-Net architecture for edge deployment in breast ultrasound image segmentation. Achieves efficient deep learning-based medical image analysis with real-time performance in resource-constrained environments.
Edge-Deployable Deep Segmentation of Breast Ultrasound Images via Optimized U-Net
This research presents an optimized U-Net architecture specifically designed for edge deployment in breast ultrasound image segmentation. The proposed method achieves efficient deep learning-based segmentation while maintaining computational efficiency suitable for edge devices, enabling real-time medical image analysis in resource-constrained environments.
Key Highlights
- Optimized U-Net architecture for edge deployment
- Efficient deep learning-based medical image segmentation
- Real-time analysis in resource-constrained environments
- Computational efficiency suitable for edge devices
Technologies Used
- Medical Image Segmentation - Advanced image analysis for healthcare
- U-Net - Convolutional neural network architecture
- Edge Computing - Resource-efficient computing deployment
- Breast Ultrasound - Medical imaging modality
- Deep Learning - Neural network-based learning
- Edge Deployment - Optimized deployment for edge devices
Read the full research paper on ResearchGate!