Diabetic Retinopathy Detection From Eye Fundus Images Using Convolutional Neural Network
Author(s):
S. Gayathri Devi, B. Nuthan Prasad, B. Vinay Kumar, V. Vinod
Keywords:
computer-aided diagnosis; convolutional neural networks; deep learning; diabetic retinopathy; diabetic retinopathy classification; diabetic retinopathy lesions localization
Abstract
Automated Diabetic Retinopathy (DR) detection, screening and diagnosis are critical to save vision loss of patients and assist the ophthalmologists in mass screening. DR screening aims at early treatment of the disease by detecting it before the stage progresses. Present DR analysis systems use digital fundus images for diagnosis reducing the high cost of manual computation. Researchers are continuously persisting for automated screening systems which can reduce the subjective interpretation for ophthalmologists. The proposed system consists of preprocessing, extraction of candidate lesions, feature set formulation, and classification. In preprocessing, the system eliminates background pixels and extracts the blood vessels and optic disc from the digital retinal image. CNN model proposed in this paper provides an accuracy of 87.5% with cross entropy loss of 0.6370 with processing time of 1 minute and 23 seconds. Maximum accuracy improvement of 13% is achieved by the proposed approach over state of the art methods demonstrating preeminence in fundus image classification.
Article Details
Unique Paper ID: 155333

Publication Volume & Issue: Volume 9, Issue 1

Page(s): 797 - 800
Article Preview & Download


Share This Article

Conference Alert

NCSST-2021

AICTE Sponsored National Conference on Smart Systems and Technologies

Last Date: 25th November 2021

SWEC- Management

LATEST INNOVATION’S AND FUTURE TRENDS IN MANAGEMENT

Last Date: 7th November 2021

Latest Publication

Go To Issue



Call For Paper

Volume 8 Issue 4

Last Date 25 September 2021

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews

Contact Details

Telephone:6351679790
Email: editor@ijirt.org
Website: ijirt.org

Policies

Important Links

Browse Rsearch papers