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Residual Network-Based Deep Learning Framework for Diabetic Retinopathy Detection
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Author(s): Keshav Kaushik (ASET, Amity University Punjab, Mohali, India), Akashdeep Bhardwaj (University of Petroleum and Energy Studies, India), Xiaochun Cheng (Middlesex University, UK), Susheela Dahiya (Graphic Era Hill University, India), Achyut Shankar (Bennett University, Greater Noida, India), Manoj Kumar (University of Wollongong in Dubai, UAE)and Tushar Mehrotra (Galgotias University, Greater Noida, India)
Copyright: 2025
Volume: 36
Issue: 1
Pages: 21
Source title:
Journal of Database Management (JDM)
Editor(s)-in-Chief: Keng Siau (Singapore Management University, Singapore)
DOI: 10.4018/JDM.368006
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Abstract
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%.
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