Loading…

A novel Adaptive Neural Network-Based Laplacian of Gaussian (AnLoG) classification algorithm for detecting diabetic retinopathy with colour retinal fundus images

Diabetic retinopathy (DR) is a human eye disease in which the eye’s retina is damaged in diabetics. Diabetic retinopathy can be diagnosed by manually interpreting retinal fundus images, even though that takes longer to diagnose. Among these, the most challenging task in diagnosing the DR disease is...

Full description

Saved in:
Bibliographic Details
Published in:Neural computing & applications 2024-03, Vol.36 (7), p.3513-3524
Main Authors: Ramasamy, Manjula Devi, Periasamy, Keerthika, Periasamy, Suresh, Muthusamy, Suresh, Ramamoorthi, Ponarun, Thangavel, Gunasekaran, Sekaran, Sreejith, Sadasivuni, Kishor Kumar, Geetha, Mithra
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Diabetic retinopathy (DR) is a human eye disease in which the eye’s retina is damaged in diabetics. Diabetic retinopathy can be diagnosed by manually interpreting retinal fundus images, even though that takes longer to diagnose. Among these, the most challenging task in diagnosing the DR disease is edge detection in retinal fundus images to identify the region of infection and its severity. This paper aims to use the adaptive neural network-based Laplacian of Gaussian (AnLoG) classification algorithm on features extracted from diverse retinal fundus images to improve DR disease diagnostic accuracy and reduce training time. Based on the retinal fundus image in the Messidor dataset, the consequence of the proposed AnLoG classification algorithm for detecting diabetic retinopathy is compared to traditional supervised BPN machine learning algorithms and other contemporary techniques. AnLoG has proved its supremacy in terms of accuracy (97.29%), recall (94.64%), precision (93.13%), and F-Score (93.80%). Simulation results show that the proposed technique performs well compared to the existing approach.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-023-09324-z