Diabetic Retinopathy Detection using Retinal Images and Deep Learning Model 
Vani Ashok1, Navneet Hosmane2, Ganesh Mahagaonkar3, Aditya Gudigar4, Anvith P5
1Vani Ashok, Assistant Professor, Department of Computer Science and Engineering at JSS Science and Technology University, Mysuru (Karnataka), India.
2Navneet Hosmane*, Student, Department of Computer Science and Engineering, JSS Science and Technology University, Mysuru (Karnataka), India.
3Ganesh Mahagaonkar, Student, Department of Computer Science and Engineering, JSS Science and Technology University, Mysuru (Karnataka), India.
4Aditya Gudigar, Student, Department of Computer Science and Engineering, JSS Science and Technology University, Mysuru (Karnataka), India.
5Anvith P, B.E., Student Department of Computer Science and Engineering, JSS Science and Technology University, Mysuru (Karnataka), India
Manuscript received on June 20, 2021. | Revised Manuscript received on June 30, 2021. | Manuscript published on July 30, 2021. | PP: 35-39 | Volume-10, Issue-9, July 2021 | Retrieval Number: 100.1/ijitee.I92960710921 | DOI: 10.35940/ijitee.I9296.0710921
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: APTOS, Convolutional Neural Networks, Diabetic Retinopathy, ResNet50.