Multi-level Quality Assessment of Retinal Fundus Images using Deep Convolution Neural Networks

Satya M. Muddamsetty, Thomas B. Moeslund

2021

Abstract

Retinal fundus image quality assessment is one of the major steps in screening for retinal diseases, since the poor-quality retinal images do not allow an accurate medical diagnosis. In this paper, we first introduce a large multi-level Retinal Fundus Image Quality Assessment (RFIQA) dataset. It has six levels of quality grades, which are based on important regions to consider for diagnosing diabetic retinopathy (DR), Aged Macular Degeneration (AMD) and Glaucoma by ophthalmologists. Second, we propose a Convolution Neural Network (CNN) model to assess the quality of the retinal images with much fewer parameters than existing deep CNN models and finally we propose to combine deep and generic texture features, and using Random Forest classifier. Experiments show that combing both deep and generic features outperforms using any of the two feature types in isolation. This is confirmed on our new dataset as well as on other public datasets.

Paper Citation

in Harvard Style

Muddamsetty S. and Moeslund T. (2021). Multi-level Quality Assessment of Retinal Fundus Images using Deep Convolution Neural Networks. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP; ISBN 978-989-758-488-6, SciTePress, pages 661-668. DOI: 10.5220/0010250506610668

in Bibtex Style

@conference{visapp21,
author={Satya M. Muddamsetty and Thomas B. Moeslund},
title={Multi-level Quality Assessment of Retinal Fundus Images using Deep Convolution Neural Networks},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP},
year={2021},
pages={661-668},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010250506610668},
isbn={978-989-758-488-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP
TI - Multi-level Quality Assessment of Retinal Fundus Images using Deep Convolution Neural Networks
SN - 978-989-758-488-6
AU - Muddamsetty S.
AU - Moeslund T.
PY - 2021
SP - 661
EP - 668
DO - 10.5220/0010250506610668
PB - SciTePress