One-class kernel subspace ensemble for medical image classification

Zhang, Yungang, Zhang, Bailing, Coenen, Frans ORCID: 0000-0003-1026-6649, Xiao, Jimin and Lu, Wenjin
(2014) One-class kernel subspace ensemble for medical image classification. EURASIP Journal on Advances in Signal Processing, 2014 (1). 17-.

[thumbnail of eurasipJopaper2014.pdf] Text
eurasipJopaper2014.pdf - Published version

Download (1MB)


Classification of medical images is an important issue in computer-assisted diagnosis. In this paper, a classification scheme based on a one-class kernel principle component analysis (KPCA) model ensemble has been proposed for the classification of medical images. The ensemble consists of one-class KPCA models trained using different image features from each image class, and a proposed product combining rule was used for combining the KPCA models to produce classification confidence scores for assigning an image to each class. The effectiveness of the proposed classification scheme was verified using a breast cancer biopsy image dataset and a 3D optical coherence tomography (OCT) retinal image set. The combination of different image features exploits the complementary strengths of these different feature extractors. The proposed classification scheme obtained promising results on the two medical image sets. The proposed method was also evaluated on the UCI breast cancer dataset (diagnostic), and a competitive result was obtained. © 2014 Zhang et al.; licensee Springer.

Item Type: Article
Additional Information: ## TULIP Type: Articles/Papers (Journal) ##
Uncontrolled Keywords: Breast Cancer, Cancer, Biomedical Imaging, Women's Health, Bioengineering, 4 Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, Cancer
Depositing User: Symplectic Admin
Date Deposited: 02 Feb 2017 16:34
Last Modified: 06 Jun 2024 16:24
DOI: 10.1186/1687-6180-2014-17
Related URLs: