Open AccessDiabetic Retinopathy Detection using Retinal Images and Deep Learning Model
Author(s) -
Vani Ashok,
Navneet Hosmane,
Ganesh Mahagaonkar,
Aditya Gudigar,
P Anvith
Publication year - 2021
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.i9296.0710921
Subject(s) - diabetic retinopathy , convolutional neural network , artificial intelligence , deep learning , computer science , fundus (uterus) , benchmark (surveying) , transfer of learning , blindness , retinopathy , kappa , population , pattern recognition (psychology) , machine learning , optometry , ophthalmology , diabetes mellitus , medicine , mathematics , cartography , geometry , environmental health , geography , endocrinology
Diabetic Retinopathy (DR) is one of the serious problems caused by diabetes and a leading source of blindness in the working-age population of the advanced world. Detecting DR in the early stages is crucial since the disease generally shows few symptoms until it is too late to provide an effective cure. But detecting DR requires a skilled clinician to examine and assess digital color fundus images of the retina. By simplifying the detection process, severe damages to the eyes can be prevented. Many deep learning models particularly Convolutional Neural Networks (CNNs) have been tested in similar fields as well as in the detection of DR in early stages. In this paper, we propose an automatic model for detecting and suggesting different stages of DR. The work has been carried out on APTOS 2019 Blindness Detection Benchmark Dataset which contains around 3600 retinal images graded by clinicians for the severity of diabetic retinopathy on a range of 0 to 4. The proposed method uses ResNet50 (Residual Network that is 50 layers deep) CNN model along with pre-trained weights as the base neural network model. Due to its depth and better transfer learning capabilities, the proposed model with ResNet50 achieved 82% classification accuracy. The classification ability of the model was further analysed with Cohen Kappa score. The optimized validation Cohen Kappa score of 0.827 indicate that the proposed model didn’t predict the outputs by chance.