Sparse Contribution Feature Selection and Classifiers Optimized by Concave-Convex Variation for HCC Image Recognition
Joint Authors
Li, Siqi
Pang, Wenbo
Jiang, Huiyan
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-07-17
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Accurate classification of hepatocellular carcinoma (HCC) image is of great importance in pathology diagnosis and treatment.
This paper proposes a concave-convex variation (CCV) method to optimize three classifiers (random forest, support vector machine, and extreme learning machine) for the more accurate HCC image classification results.
First, in preprocessing stage, hematoxylin-eosin (H&E) pathological images are enhanced using bilateral filter and each HCC image patch is obtained under the guidance of pathologists.
Then, after extracting the complete features of each patch, a new sparse contribution (SC) feature selection model is established to select the beneficial features for each classifier.
Finally, a concave-convex variation method is developed to improve the performance of classifiers.
Experiments using 1260 HCC image patches demonstrate that our proposed CCV classifiers have improved greatly compared to each original classifier and CCV-random forest (CCV-RF) performs the best for HCC image recognition.
American Psychological Association (APA)
Pang, Wenbo& Jiang, Huiyan& Li, Siqi. 2017. Sparse Contribution Feature Selection and Classifiers Optimized by Concave-Convex Variation for HCC Image Recognition. BioMed Research International،Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1139703
Modern Language Association (MLA)
Pang, Wenbo…[et al.]. Sparse Contribution Feature Selection and Classifiers Optimized by Concave-Convex Variation for HCC Image Recognition. BioMed Research International No. 2017 (2017), pp.1-14.
https://search.emarefa.net/detail/BIM-1139703
American Medical Association (AMA)
Pang, Wenbo& Jiang, Huiyan& Li, Siqi. Sparse Contribution Feature Selection and Classifiers Optimized by Concave-Convex Variation for HCC Image Recognition. BioMed Research International. 2017. Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1139703
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139703