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Deep Learning Model for Diagnosing the Severity of Diabetic Retinopathy

Publication Type : Journal Article

Publisher : SPRINGER

Source : Fourth Congress on Intelligent Systems. CIS 2023. Lecture Notes in Networks and Systems (SPRINGER), vol 868, 2024, (SCOPUS Indexed)

Url : https://link.springer.com/chapter/10.1007/978-981-99-9037-5_32

Campus : Chennai

School : School of Engineering

Department : Electronics and Communication

Year : 2024

Abstract : Diabetes Mellitus, a condition that affects diabetic individuals, causes diabetic retinopathy. In many nations, it is the leading cause of blindness. A person’s likelihood of going blind increases if they have diabetic retinopathy. The failure to properly treat and monitor diabetic retinopathy before it progressed to a severe stage was the root cause of blindness in 90% of instances. In its extreme stages, diabetic retinopathy has no known treatment. However, it can be identified and avoided in its early stages. Consequently, computerized computer diagnosis will help clinicians to find diabetic retinopathy early and more affordably. In health informatics, deep learning is gaining importance. This study uses different deep learning models to create and compare, from which DenseNet169 has been identified as the best deep learning model to categorize the disease into different stages. It may help clinicians to determine the severity of diabetic retinopathy more effectively.

Cite this Research Publication : Nikitha Reddy Nalla and Ganesh Kumar Chellamani, “Deep Learning Model for Diagnosing the Severity of Diabetic Retinopathy,” Fourth Congress on Intelligent Systems. CIS 2023. Lecture Notes in Networks and Systems (SPRINGER), vol 868, 2024, (SCOPUS Indexed)

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