A diagnosis model for detection and classification of diabetic retinopathy using deep learning

Diabetes mellitus (DM) is an immense progressive disease that affects the usage of blood glucose as energy, resulting in surplus glucose in the blood. If prolonged diabetes, it causes damage to both larger and smaller blood vessels, known as macrovascular and microvascular complications, respectivel...

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Bibliographic Details
Published in:Network modeling and analysis in health informatics and bioinformatics (Wien) 2023-10, Vol.12 (1), p.37, Article 37
Main Authors: Syed, Saba Raoof, M A, Saleem Durai
Format: Article
Language:English
Subjects:
Algorithms
Applications of Graph Theory and Complex Networks
Artificial neural networks
Bioinformatics
Blood vessels
Classification
Computational Biology/Bioinformatics
Computer Science
Datasets
Deep learning
Diabetes
Diabetes mellitus
Diabetes mellitus (non-insulin dependent)
Diabetic retinopathy
Glucose
Health Informatics
Insulin
Machine learning
Methods
Microvasculature
Neural networks
Optimization techniques
Original Article
Retinopathy
Segmentation
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Summary:Diabetes mellitus (DM) is an immense progressive disease that affects the usage of blood glucose as energy, resulting in surplus glucose in the blood. If prolonged diabetes, it causes damage to both larger and smaller blood vessels, known as macrovascular and microvascular complications, respectively. The main objective of this paper is to develop an automated method for the detection, segmentation, and severity classification of type 2 diabetes mellitus (T2DM) microvascular complication Diabetic Retinopathy (DR) using the EyePACS dataset. An RU-Net (Residual U-Net) is proposed for segmentation, and a CCNN (Concatenated Convolutional Neural Network) for multi-class classification of DR. The proposed classification method recorded 0.9881% and 0.9683% accuracy for benchmark and real-time data. The result demonstrates that the proposed model is appropriate to assist physicians in the detection and classification of DR accurately and promptly.
ISSN:2192-6670
2192-6662
2192-6670
DOI:10.1007/s13721-023-00432-3