DRSegNet: A cutting-edge approach to Diabetic Retinopathy segmentation and classification using parameter-aware Nature-Inspired optimization

Diabetic retinopathy (DR) is a prominent reason of blindness globally, which is a diagnostically challenging disease owing to the intricate process of its development and the human eye’s complexity, which consists of nearly forty connected components like the retina, iris, optic nerve, and so on. Th...

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Bibliographic Details
Published in:PloS one Vol. 19; no. 12; p. e0312016
Main Authors: Kamal, Sundreen Asad, Du, Youtian, Khalid, Majdi, Farrash, Majed, Dhelim, Sahraoui
Format: Journal Article
Language:English
Published: United States Public Library of Science 05.12.2024
Public Library of Science (PLoS)
Subjects:
Algorithms
Artificial intelligence
Biology and Life Sciences
Blindness
Blood vessels
Care and treatment
Classification
Clustering
Complications and side effects
Computer and Information Sciences
Datasets
Deep learning
Diabetes
Diabetes mellitus
Diabetic retinopathy
Diabetic Retinopathy - diagnosis
Diabetic Retinopathy - diagnostic imaging
Diagnosis
Diagnostic imaging
Disease
Encoders-Decoders
Exercise
Generative adversarial networks
Health aspects
Humans
Image analysis
Image processing
Image Processing, Computer-Assisted - methods
Machine Learning
Mammography
Medical imaging
Medicine and Health Sciences
Methods
Neural networks
Neural Networks, Computer
Optic nerve
Physical Sciences
Prevention
Research and Analysis Methods
Retina
Retina - diagnostic imaging
Retina - pathology
Retinopathy
Risk factors
Sensitivity analysis
Synthetic data
Transfer learning
China
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Description
Summary:Diabetic retinopathy (DR) is a prominent reason of blindness globally, which is a diagnostically challenging disease owing to the intricate process of its development and the human eye’s complexity, which consists of nearly forty connected components like the retina, iris, optic nerve, and so on. This study proposes a novel approach to the identification of DR employing methods such as synthetic data generation, K- Means Clustering-Based Binary Grey Wolf Optimizer (KCBGWO), and Fully Convolutional Encoder-Decoder Networks (FCEDN). This is achieved using Generative Adversarial Networks (GANs) to generate high-quality synthetic data and transfer learning for accurate feature extraction and classification, integrating these with Extreme Learning Machines (ELM). The substantial evaluation plan we have provided on the IDRiD dataset gives exceptional outcomes, where our proposed model gives 99.87% accuracy and 99.33% sensitivity, while its specificity is 99. 78%. This is why the outcomes of the presented study can be viewed as promising in terms of the further development of the proposed approach for DR diagnosis, as well as in creating a new reference point within the framework of medical image analysis and providing more effective and timely treatments.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0312016