Fig. 1. Design of Convolutional Neural Network for Polyp Detection
Fig. 2. Conventional Polyp Detection Method
Fig. 3. Edge Detection Process
Fig. 4. Image Generation of Changed Aspect Ratio and Circle Detection Result
Fig. 5. Circle Analysis Direction
Fig. 6. Circle Analysis Process for a Direction
Fig. 7. Merge Single Suspicious Regions
Fig. 8. PSR Evaluation
Fig. 9. Examples of Images Showing High F1-Score
Fig. 10. Examples of Images Showing Low F1-Score
Fig. 11. PSR Image
Fig. 12. PSR-Weighted Images
Table 1. Analysis of Learning Performance for PSR-weighted Image by SVM
Table 2. Analysis of Learning Performance for PSR-weighted Image by RF
Table 3. Analysis of Learning Performance for PSR-weighted Images as Performance of PSR
참고문헌
- Yixuan Yuan, Baopu Li, and Max Q-H. Meng, "Improved Bag of Feature for Automatic Polyp Detection in Wireless Capsule Endoscopy Images," IEEE Transactions on Automation Science and Engineering, Vol.13. No.2, pp.529-535, 2016. https://doi.org/10.1109/TASE.2015.2395429
- Mohamed El Ansari and Said Charfi, "Computer-aided System for Polyp Detection in Wireless Capsule Endoscopy Images," Wireless Networks and Mobile Communications (WINCOM), 2017 International Conference on. IEEE, 2017.
- Meryem Souaidi, Said Charfi, et al., "New Features for Wireless Capsule Endoscopy Polyp Detection," Intelligent Systems and Computer Vision (ISCV), 2018 International Conference on. IEEE, 2018.
- Santi Segui, Michal Drozdzal, et al., "Generic Feature Learning for Wireless Capsule Endoscopy Analysis," Computers in Biology and Medicine, Vol.79, pp.163-172, 2016. https://doi.org/10.1016/j.compbiomed.2016.10.011
- Yixuan Yuan and Max Q H. Meng, "Deep Learning for Polyp Recognition in Wireless Capsule Endoscopy Images," Medical Physics., Vol.44. No.4, pp.1379-1389, 2017. https://doi.org/10.1002/mp.12147