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Residual Network-Based Deep Learning Framework for Diabetic Retinopathy Detection

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Publicado en:Journal of Database Management vol. 36, no. 1 (2025), p. 1-22
Autor principal: Bhardwaj, Akashdeep
Otros Autores: Kumar, Manoj, Kaushik, Keshav, Cheng, Xiaochun, Dahiya, Susheela, Shankar, Achyut, Mehrotra, Tushar
Publicado:
IGI Global
Materias:
Accuracy
Diabetes
Tomography
Deep learning
Datasets
Blood vessels
Health services
Medical diagnosis
Diabetic retinopathy
Machine learning
Medical personnel
Medical imaging
Blindness
Efficiency
Disease
Health care industry
Artificial intelligence
Valuation
Retinopathy
Robotics
Robots
Neural networks
Classification
Support vector machines
Databases
Patients
Algorithms
Acceso en línea:Citation/Abstract
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Resumen:Artificial intelligence and machine learning have been transforming the health care industry in many areas such as disease diagnosis with medical imaging, surgical robots, and maximizing hospital efficiency. The Healthcare service market utilizing Artificial Intelligence is expected to reach 45.2 billion U. S. Dollars by 2026 from its current valuation, off $4.9 billion. Diabetic Retinopathy (DR) is a disease that results from complications of type one and Type two diabetes and affects patients' eyes. Diabetic retinopathy, if remains unaddressed, is one of the most serious complications of diabetes, resulting in permanent blindness. The disease has been affecting the lives of 347 million people worldwide. The paper aims to propose a residual network-based deep learning framework for the detection of diabetic retinopathy. The accuracy of our approach is 83% whereas the precision value for checking the absence of DR is 95%.
ISSN:1063-8016
1533-8010
1047-9430
DOI:10.4018/JDM.368006
Fuente:ABI/INFORM Global