Ensemble Learning Optimization for Diabetic Retinopathy Image Analysis

Hanan S. Alghamdi, Lilian Tang, Yaochu Jin

2015

Abstract

Ensemble Learning has been proved to be an effective solution to learning problems. Its success is mainly dependent on diversity. However, diversity is rarely evaluated and explicitly used to enhance the ensemble performance. Diabetic Retinopathy (DR) automatic detection is one of the important applications to support the health care services. In this research, some existing statistical diversity measures were utilized to optimize ensembles used to detect DR related signs. Ant Colony Optimization (ACO) algorithm is adopted to select the ensemble base models using various criteria. This paper evaluates several optimized and non-optimized ensemble structures used for vessel segmentation. The results demonstrate the necessity of adopting the ensemble learning and the advantage of ensemble optimization to support the DR related signs detection.

Paper Citation

in Harvard Style

S. Alghamdi H., Tang L. and Jin Y. (2015). Ensemble Learning Optimization for Diabetic Retinopathy Image Analysis . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 471-477. DOI: 10.5220/0005296604710477

in Bibtex Style

@conference{visapp15,
author={Hanan S. Alghamdi and Lilian Tang and Yaochu Jin},
title={Ensemble Learning Optimization for Diabetic Retinopathy Image Analysis},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={471-477},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005296604710477},
isbn={978-989-758-089-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Ensemble Learning Optimization for Diabetic Retinopathy Image Analysis
SN - 978-989-758-089-5
AU - S. Alghamdi H.
AU - Tang L.
AU - Jin Y.
PY - 2015
SP - 471
EP - 477
DO - 10.5220/0005296604710477