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E1241103513.pdf
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🔗 Source: ijitee.org
📊 Size: 288 KB
👤 Author: IJSCE
⬇️ Downloads: 108

Summary

The evolution of digital computers and modern learning theories led to Computational Intelligence (CI) engineering, which can aid in liver surgery by analyzing preoperative data. Liver fibrosis extraction is done using image enhancement techniques, such as filtering, clustering, and supervised techniques like ANN and SVM. A statistical model of liver fibrosis is constructed from MRI scans, and the system is tested on over 300 digitized MRI images. The proposed system can be used for non-invasive classification and progression monitoring of liver fibrosis, and it outperforms existing image-based methods. Magnetic Resonance Imaging (MRI) is used to diagnose liver fibrosis, and image segmentation is an important process to extract information from complex medical images. A novel automatic method for liver fibrosis classification and grading from MRI maps is proposed, using supervised and unsupervised learning methods. The system is evaluated using a hospital MRI image database, and the results show that the proposed system can provide accurate and early detection of liver fibrosis.

Description

The evolution of digital computers and modern learning theories led to Computational Intelligence (CI) engineering, which can aid in liver surgery by analyzing...

Technical Information

  • File Format: PDF
  • File Size: 288 KB
  • Pages: 6
  • Language: EN
  • Author: IJSCE
  • Total Downloads: 108
  • Last Updated: 6 days ago

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