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By: Rama Devi C, Rithanya K.R, Shakti P, and Subhashini G.
1 Assistant Professor, Department of EEE, St. Joseph’s College of Engineering, Chennai, Tamil Nadu, India
2 Student, Department of EEE, St. Joseph’s College of Engineering, Chennai, Tamil Nadu, India
3 Student, Department of EEE, St. Joseph’s College of Engineering, Chennai, Tamil Nadu, India
4 Assistant Professor, Department of IT, St. Joseph’s Institute of Technology, Chennai, Tamil Nadu, India
Diabetic retinopathy (DR) is a leading cause of blindness worldwide, impacting the largest number of individuals who have diabetes. Early identification of diabetic retinopathy may help to prevent severe effects; however, the very limited availability of labeled datasets, and the possibility of overfitting of the model, presents obstacles to obtaining an accurate diagnosis. In this research, a hybrid deep learning strategy is introduced, where Generative Adversarial Networks (GANs) are used for data augmentation and ResNet-50-based Convolutional Neural Networks (CNNs) for DR classification. The GANs produce artificial retinal images, promoting dataset diversity and overfitting prevention, while the ResNet-50 model is optimized for effective feature extraction from fundus images. The objective is to enhance the accuracy and sensitivity of detection of DR. The system proposed performs better than conventional CNNs and other models such as VGG16 and InceptionV3, with an accuracy of 86.7% in the Kaggle DR dataset. The benefits of the system are improved performance, less overfitting, and the capability for early and effective DR detection that could result in improved clinical results.
Citation:
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