Eye Abnormality Automatic Detection using Deep Learning based Model

Gany, Audyati and Hasugian, Meilan Jimmy and Sartika, Erwani Merry and Pasaribu, Novie Theresia Br. and Georgina, Hannah (2021) Eye Abnormality Automatic Detection using Deep Learning based Model. In: 1st International Conference on Emerging Issues in Humanity Studies and Social Sciences, 1-2 July 2021, Bandung.

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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.

Item Type:	Conference or Workshop Item (Paper)
Contributors:	Contribution Contributors NIDN/NIDK Email UNSPECIFIED Gany, Audyati UNSPECIFIED UNSPECIFIED UNSPECIFIED Hasugian, Meilan Jimmy UNSPECIFIED UNSPECIFIED UNSPECIFIED Sartika, Erwani Merry UNSPECIFIED UNSPECIFIED UNSPECIFIED Pasaribu, Novie Theresia Br. UNSPECIFIED UNSPECIFIED UNSPECIFIED Georgina, Hannah UNSPECIFIED UNSPECIFIED
Subjects:	T Technology > T Technology (General)
Depositing User:	Perpustakaan Maranatha
Date Deposited:	05 Apr 2023 08:19
Last Modified:	10 Apr 2023 08:38
URI:	http://repository.maranatha.edu/id/eprint/31651

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