IJEEDC Enhancing Diabetic Retinopathy Detection With CNN-Based Models: A Comparative Study Of UNET And Stacked UNET Architectures

Journal Paper

Paper Title :Enhancing Diabetic Retinopathy Detection With CNN-Based Models: A Comparative Study Of UNET And Stacked UNET Architectures

Author :S. Navaneetha Krishnan, Ameya Uppina, Talluri Krishna Sai Teja, Nikhil N Iyer, Joe Dhanith P R

Article Citation :S. Navaneetha Krishnan ,Ameya Uppina ,Talluri Krishna Sai Teja ,Nikhil N Iyer ,Joe Dhanith P R , (2024 ) " Enhancing Diabetic Retinopathy Detection With CNN-Based Models: A Comparative Study Of UNET And Stacked UNET Architectures " , International Journal of Electrical, Electronics and Data Communication (IJEEDC) , pp. 1-6, Volume-12,Issue-10

Abstract : Diabetic Retinopathy (DR) is a severe complication of diabetes. Damaged or abnormal blood vessels can cause loss of vision. The need for massive screening of a large population of diabetic patients has generated an interest in a computer-aided fully automatic diagnosis of DR. In the realm of Deep learning frameworks, particularly convolutional neural networks (CNNs), have shown great interest and promise in detecting DR by analyzing retinal images. However, several challenges have been faced in the application of deep learning in this domain. High-quality, annotated datasets are scarce, and the variations in image quality and class imbalances pose significant hurdles in developing a dependable model. In this paper, we demonstrate the proficiency of two Convolutional Neural Networks (CNNs) based models – UNET and Stacked UNET utilizing the APTOS (Asia Pacific Tele-Ophthalmology Society) Dataset. This system achieves an accuracy of 92.81% for the UNET and 93.32% for the stacked UNET architecture. The architecture classifies the images into five categories ranging from 0 to 4, where 0 is no DR and 4 is proliferative DR. Keywords - Convolutional Neural Networks (CNN), Diabetic Retinopathy, Deep Learning, UNET

Type : Research paper

Published : Volume-12,Issue-10


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Mar. 2025
Submitted Papers	:	80
Accepted Papers	:	10
Rejected Papers	:	70
Acc. Perc	:	12%
Issue Published	:	143
Paper Published	:	1781
No. of Authors	:	4956

Published : Volume-12,Issue-10

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