Automated Diabetic Retinopathy Detection Using Convolutional Neural Networks For Feature Extraction And Classification (ADRFEC)

P.AnilKumar

doi:10.36948/ijfmr.2024.v06i05.27731

Automated Diabetic Retinopathy Detection Using Convolutional Neural Networks For Feature Extraction And Classification (ADRFEC)

Author(s)	P.AnilKumar
Country	India
Abstract	Diabetic Retinopathy (DR) is a significant complication of Diabetes Mellitus, leading to various retinal abnormalities that can impair vision and, in severe cases, result in blindness. Approximately 80% of patients with long-standing diabetes for 10–15 years develop DR. The manual process of diagnosing and detecting DR for timely treatment is both time-consuming and unreliable, mainly due to resource constraints and the need for expert opinion. To address this challenge, computerized diagnostic systems utilizing Deep Learning (DL) Convolutional Neural Network (CNN) architectures have been proposed to learn DR patterns from fundus images and assess disease severity. The proposed model performs an exhaustive analysis of these architectures upon fundus images, and derives the best performing DL architecture for DR feature extraction and fundus image classification. Amongst all the models, ResNet50 has achieved the highest training accu- racy whereas VGG-16 has achieved the lowest training accuracy. Again, VGG-16 has achieved lowest validation accuracy whereas ResNet101 has achieved highest validation accuracy.
Keywords	Diabetic Retinopathy, Fundus image, Convolutional Neural Network, Deep Learning, Image classification
Field	Computer Applications
Published In	Volume 6, Issue 5, September-October 2024
Published On	2024-09-21
Cite This	Automated Diabetic Retinopathy Detection Using Convolutional Neural Networks For Feature Extraction And Classification (ADRFEC) - P.AnilKumar - IJFMR Volume 6, Issue 5, September-October 2024. DOI 10.36948/ijfmr.2024.v06i05.27731
DOI	https://doi.org/10.36948/ijfmr.2024.v06i05.27731
Short DOI	https://doi.org/g4qmjh

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Automated Diabetic Retinopathy Detection Using Convolutional Neural Networks For Feature Extraction And Classification (ADRFEC)

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