Application of Multi-Classification Support Vector Machine in the B-Placenta Image Classification
Abstract
In this paper, B-placenta image is classified automatically using support vector machine based on feature extraction. Firstly, artificial selected region of interest (ROI) is regarded as the object of feature extraction. Then traditional gray-scale statistical analysis is used to extract the characteristic parameters of B-placenta image as the basis data for the placenta classification. The binary tree multi-classification SVM is used to automatically classify the B-placenta image. The binary tree generation algorithm is optimized based on the ultra-radius. The experiment shows that this classifying method using binary tree multi-classification SVM in the B-placenta image classification has very high value.
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Liu et al. automatically classified placental maturity using an SVM classifier and obtained a classification rate of 90% based on gray variance, distortions, and kurtosis.
“Liu et al.15 proposed to automatically classify placental maturity using SVM classifier. A classification rate of 90% had been obtained based on the three quantitative parameters: gray variance, distortions, and kurtosis.”
From Discriminative Learning for Automatic Staging of Placental Maturity via Multi-layer Fisher Vector (2015) · cites this paper as [15]
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Discriminative learning for automatic staging of placental maturity via multi-layer fisher vector
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- Authors
- Liu, Z., Zheng, H., & Lin, S.
- Affiliations
- Zhejiang University of Science and Technology (CN)
- Venue
- 2009 International Conference on Computational Intelligence and Software Engineering
- Publisher
- IEEE
- Published
- 2009-12
- Pages
- 1-4
- OpenAlex
- W2124642068
- Cited by
- 8
- References
- 5
- Citations by year
- 2020: 1 · 2018: 1 · 2016: 2 · 2015: 2 · 2014: 2
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on Computational Intelligence and Software Engineering (CiSE) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- support vector machine, multi-class classification, binary trees, feature extraction, region of interest
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