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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Vydáno v:	PloS one Ročník 19; číslo 12; s. e0312016
Hlavní autoři:	Kamal, Sundreen Asad, Du, Youtian, Khalid, Majdi, Farrash, Majed, Dhelim, Sahraoui
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 05.12.2024 Public Library of Science (PLoS)
Témata:	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
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: The authors have declared that no competing interests exist.
ISSN:	1932-6203 1932-6203
DOI:	10.1371/journal.pone.0312016