title: Exploiting ensemble learning for automatic cataract detection and grading.
abstract: Cataract is defined as a lenticular opacity presenting usually with poor visual acuity. It is one of the most common causes of visual impairment worldwide. Early diagnosis demands the expertise of trained healthcare professionals, which may present a barrier to early intervention due to underlying costs. To date, studies reported in the literature utilize a single learning model for retinal image classification in grading cataract severity. We present an ensemble learning based approach as a means to improving diagnostic accuracy. Three independent feature sets, i.e., wavelet-, sketch-, and texture-based features, are extracted from each fundus image. For each feature set, two base learning models, i.e., Support Vector Machine and Back Propagation Neural Network, are built. Then, the ensemble methods, majority voting and stacking, are investigated to combine the multiple base learning models for final fundus image classification. Empirical experiments are conducted for cataract detection (two-class task, i.e., cataract or non-cataractous) and cataract grading (four-class task, i.e., non-cataractous, mild, moderate or severe) tasks. The best performance of the ensemble classifier is 93.2% and 84.5% in terms of the correct classification rates for cataract detection and grading tasks, respectively. The results demonstrate that the ensemble classifier outperforms the single learning model significantly, which also illustrates the effectiveness of the proposed approach.
authors.affiliation: Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing, China. Electronic address: yangjijiang@tsinghua.edu.cn.
authors.forename: Ji-Jiang
authors.initials: JJ
authors.lastname: Yang
authors.affiliation: School of Software Engineering, Beijing University of Technology, Beijing, China. Electronic address: lijianqiang@bjut.edu.cn.
authors.forename: Jianqiang
authors.initials: J
authors.lastname: Li
authors.affiliation: Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing, China. Electronic address: ruifangshen@163.com.
authors.forename: Ruifang
authors.initials: R
authors.lastname: Shen
authors.affiliation: Automation School, Beijing University of Posts and Telecommunications, Beijing 100876, China. Electronic address: zak47y@163.com.
authors.forename: Yang
authors.initials: Y
authors.lastname: Zeng
authors.affiliation: School of Software Engineering, Beijing University of Technology, Beijing, China. Electronic address: jianhee@bjut.edu.cn.
authors.forename: Jian
authors.initials: J
authors.lastname: He
authors.affiliation: School of Software Engineering, Beijing University of Technology, Beijing, China. Electronic address: bijing@bjut.edu.cn.
authors.forename: Jing
authors.initials: J
authors.lastname: Bi
authors.affiliation: School of Software Engineering, Beijing University of Technology, Beijing, China. Electronic address: li.yong@bjut.edu.cn.
authors.forename: Yong
authors.initials: Y
authors.lastname: Li
authors.affiliation: Automation School, Beijing University of Posts and Telecommunications, Beijing 100876, China. Electronic address: lqflucky@163.com.
authors.forename: Qinyan
authors.initials: Q
authors.lastname: Zhang
authors.affiliation: Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing, China. Electronic address: lihuipeng@tsinghua.edu.cn.
authors.forename: Lihui
authors.initials: L
authors.lastname: Peng
authors.affiliation: Research Institute of Application Technology in Wuxi, Tsinghua University, Jiangsu, China. Electronic address: qing.wang@tsinghua.edu.cn.
authors.forename: Qing
authors.initials: Q
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D002386:Cataract / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D009887:Ophthalmoscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Cataract detection; Ensemble learning; Fundus image classification; Neural network; Support vector machines