Eye Abnormality Automatic Detection using Deep Learning based Model
| Main Authors: | Gany, Audyati , Hasugian, Meilan Jimmy, Sartika, Erwani Merry, Pasaribu, Novie Theresia Br., Georgina, Hannah |
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| Format: | Proceeding PeerReviewed Book |
| Terbitan: |
, 2021
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| Subjects: | |
| Online Access: |
http://repository.maranatha.edu/31651/1/5%20ICE-TES2021-Eye%20Abnormality%20Automatic%20Detection%20%282021%29.pdf http://repository.maranatha.edu/31651/2/6.%20Turnitin_Eye%20Abnormality%20Automatic%20Detection%20using%20Deep%20Learning%20based%20Model.pdf http://repository.maranatha.edu/31651/ |
Daftar Isi:
- 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.