Publication detail

EyeDeep-Net: A Multi-Class Diagnosis of Retinal Diseases using Deep Neural Network

N. Sengar R. C. Joshi M. K. Dutta R. Burget

Original Title

EyeDeep-Net: A Multi-Class Diagnosis of Retinal Diseases using Deep Neural Network

Type

journal article in Web of Science

Language

English

Original Abstract

Retinal images are a key element for ophthalmologists in diagnosing a variety of eye illnesses. The retina is vulnerable to microvascular changes as a result of many retinal diseases and a variety of research have been done on early diagnosis of medical images to take proper treatment on time. This paper designs an automated deep learning-based non-invasive framework to diagnose multiple eye diseases using colour fundus images. A multi-class eye disease RFMiD dataset was used to develop an efficient diagnostic framework. Multi-class fundus images were extracted from a multi-label dataset and then various augmentation techniques were applied to make the framework robust in real-time. Images were processed according to the network for low computational demand. A multi-layer neural network EyeDeep-Net has been developed to train and test images for diagnosis of various eye problems in which the keystone convolutional neural network extracts relevant features from the input colour fundus image dataset and then processed features were used to make predictive diagnostic decisions. The strength of the EyeDeep-Net is evaluated using multiple statistical parameters and the performance of the proposed model is found to be significantly superior to multiple baseline state-of-the-art models. A comprehensive comparison of the proposed methodology to the most recent methods proves its efficacy in terms of classification and disease identification through digital fundus images.

Keywords

Deep learning ; Eye diseases; Fundus; Image classification; Medical imaging; Neural networks

Authors

N. Sengar; R. C. Joshi; M. K. Dutta; R. Burget

Released

21. 1. 2023

Publisher

Springer-Verlag London Ltd., part of Springer Nature 2023

Location

London

ISBN

1433-3058

Periodical

Neural Computing and Applications

Year of study

35

Number

3

State

United Kingdom of Great Britain and Northern Ireland

Pages from

10551

Pages to

10571

Pages count

21

URL

https://link.springer.com/article/10.1007/s00521-023-08249-x

BibTex

@article{BUT181533,
  author="N. Sengar and R. C. Joshi and M. K. Dutta and R. Burget",
  title="EyeDeep-Net: A Multi-Class Diagnosis of Retinal Diseases using Deep Neural Network",
  journal="Neural Computing and Applications",
  year="2023",
  volume="35",
  number="3",
  pages="10551--10571",
  doi="10.1007/s00521-023-08249-x",
  issn="1433-3058",
  url="https://link.springer.com/article/10.1007/s00521-023-08249-x"
}