Eye Abnormality Automatic Detection using Deep Learning based Model

Audyati Gany, Meilan Hasugian, Meilan Hasugian, Erwani Sartika, Hannah Georgina

2021

Abstract

Early detection and diagnosis of ocular pathologies would enable to forestall of visual impairment. One challenge that limits the adoption of a computer-aided diagnosis tool by the ophthalmologist is, the sight-threatening rare pathologies such as central retinal artery occlusion or anterior ischemic optic neuropathy and others are usually ignored. The aim of this research is to develop methods for automatic detection of eye abnormality caused by the most common ocular disease along with the rare pathologies. For this purpose, we developed the deep learning-based model trained with Retinal Fundus Multi-disease Image Dataset (RFMiD). This dataset consists of a 1920 fundus retina images captured using three different fundus cameras with 46 conditions annotated through adjudicated consensus of two senior retinal experts. The model is built on the top of some prominent pretrained convolutional neural network (CNN) models. From the experiment, the model could achieve the accuracy level and recall 0.87, whereas precision and F1 score are 0.86, and area under receiver operating characteristic (AUROC) is 0.90. The proposed model built in deep learning structure could be a promising model in automatic classification of ocular disease based on fundus retina images.

Paper Citation

in Harvard Style

Gany A., Hasugian M., Sartika E. and Georgina H. (2021). Eye Abnormality Automatic Detection using Deep Learning based Model. In Proceedings of the 1st International Conference on Emerging Issues in Technology, Engineering and Science - Volume 1: ICE-TES, ISBN 978-989-758-601-9, pages 370-375. DOI: 10.5220/0010754400003113

in Bibtex Style

@conference{ice-tes21,
author={Audyati Gany and Meilan Hasugian and Erwani Sartika and Hannah Georgina},
title={Eye Abnormality Automatic Detection using Deep Learning based Model},
booktitle={Proceedings of the 1st International Conference on Emerging Issues in Technology, Engineering and Science - Volume 1: ICE-TES,},
year={2021},
pages={370-375},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010754400003113},
isbn={978-989-758-601-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Emerging Issues in Technology, Engineering and Science - Volume 1: ICE-TES,
TI - Eye Abnormality Automatic Detection using Deep Learning based Model
SN - 978-989-758-601-9
AU - Gany A.
AU - Hasugian M.
AU - Sartika E.
AU - Georgina H.
PY - 2021
SP - 370
EP - 375
DO - 10.5220/0010754400003113