DeepRetina: A Deep Learning Approach for Diabetic Retinopathy Detection and Stage Classification

Using color fundus imaging to identify diabetic retinopathy (DR) is a tough process that requires skilled doctors to comprehend the existence and significance of certain small characteristics. This effort is further complicated by a complex categorization system. This research aims to use convolutio...

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Bibliographic Details
Published in2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS) pp. 1 - 5
Main Authors Nithishvikraman, H., G, Sudharsan, I, Anand Joseph Daniel D
Format Conference Proceeding
LanguageEnglish
Published IEEE 18.04.2024
Subjects
CNN
Deep learning
Deep Learning (DL)
Diabetic retinopathy
Diabetic Retinopathy (DR)
Image color analysis
Image Processing
Medical treatment
Neural networks
Ophthalmology
Retinal vessels
Sensitivity
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Summary:Using color fundus imaging to identify diabetic retinopathy (DR) is a tough process that requires skilled doctors to comprehend the existence and significance of certain small characteristics. This effort is further complicated by a complex categorization system. This research aims to use convolution neural network (CNN) to consistently diagnose diabetic retinopathy and grade patients into five groups or stages. An automatically generated diagnostic may be provided by a data-enhanced CNN architecture network that can identify the complex components involved in the class task, as well as exudates, hemorrhages, and micro-aneurysms in the retina, without requiring human input. This work trained CNN using data that was made accessible to the public. When compared to other algorithms on the same dataset, it demonstrated an astounding performance. It was used to the global dataset and obtained a great accuracy of 97% on the validation image.
DOI:10.1109/ADICS58448.2024.10533579