Diabetic retinopathy detection and grading system using deep learning approach

The segmentation time period of both exudates and macula region in retinal image is low when comparing with other segmentation methods. Moreover, this proposed segmentation and diagnosis method was tested with other DIARETDB1, Retinal Fundus Multi-disease Image, and Fundus Image Registration Dataset datasets to validate the effectiveness of the proposed method.

Authors

Institutions

Publication Details

Journal
Digital Health
Published
2026-01-01
DOI
https://doi.org/10.1177/20552076251410982
Citations
1
Primary Topic
Retinal Imaging and Analysis
Type
article
Field-Weighted Citation Impact
31.92
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Diabetic retinopathy detection and grading system using deep learning approach

K. Martin Sagayam, Hien Dang, S. Kumarganesh, D. Siva Sundhara Raja
1 citations
Digital Health
Retinal Imaging and Analysis
31.92
article

Diabetic retinopathy detection and grading system using deep learning approach

K. Martin Sagayam, Hien Dang, S. Kumarganesh, D. Siva Sundhara Raja
article en
1 citations

Abstract

The segmentation time period of both exudates and macula region in retinal image is low when comparing with other segmentation methods. Moreover, this proposed segmentation and diagnosis method was tested with other DIARETDB1, Retinal Fundus Multi-disease Image, and Fundus Image Registration Dataset datasets to validate the effectiveness of the proposed method.

Digital HealthVol. 12
Karunya University (IN), Molloy University (US)
Openalex Percentile: Top 2%
Retinal Imaging and Analysis
31.92
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.