Diabetic Retinopathy Detection Using CNN with Residual Block with DCGAN
This work addresses the problem of early diabetic retinopathy detection for healthcare professionals, especially in resource-constrained settings, but it is incremental as it combines existing methods like CNNs and DCGANs.
The authors tackled automated detection and classification of diabetic retinopathy using a CNN with residual blocks and DCGAN for data augmentation, achieving a model that classifies retinal images into five categories to support early diagnosis.
Diabetic Retinopathy (DR) is a major cause of blindness worldwide, caused by damage to the blood vessels in the retina due to diabetes. Early detection and classification of DR are crucial for timely intervention and preventing vision loss. This work proposes an automated system for DR detection using Convolutional Neural Networks (CNNs) with a residual block architecture, which enhances feature extraction and model performance. To further improve the model's robustness, we incorporate advanced data augmentation techniques, specifically leveraging a Deep Convolutional Generative Adversarial Network (DCGAN) for generating diverse retinal images. This approach increases the variability of training data, making the model more generalizable and capable of handling real-world variations in retinal images. The system is designed to classify retinal images into five distinct categories, from No DR to Proliferative DR, providing an efficient and scalable solution for early diagnosis and monitoring of DR progression. The proposed model aims to support healthcare professionals in large-scale DR screening, especially in resource-constrained settings.