Multi-constraints based deep learning model for automated segmentation and diagnosis of coronary artery disease in X-ray angiographic images

Engineering

• Input Image 100%
• Deep Learning Method 100%
• Metrics 50%
• Signal-to-Noise Ratio 50%
• Simulation Tool 50%
• Similarities 50%
• Edge Information 50%
• Enhanced Image 50%
• Peak Signal 50%
• Mean Square Error 50%
• Noise Ratio 50%

Computer Science

• Deep Learning Model 100%
• segmentation accuracy 100%
• Simulation Tool 33%
• segmented image 33%
• VGG-16 33%
• Jaccard Similarity 33%
• Evaluation Metric 33%
• peak signal to noise ratio 33%
• Hausdorff Distance 33%
• binarization 33%
• Edge Information 33%
• U-Net 33%
• Enhanced Image 33%

Earth and Planetary Sciences

• X Ray 100%
• U-Net 25%