IMDE-UGAN: Improved Memetic Direction Exploitation Optimized U-Net Generative Adversarial Network for Classification of Diabetic Retinopathy

Abstract

The most important human organ is the eye which is essential for the processing and absorbing of external visual information. Diabetic retinopathy (DR) is the primary factor contributing to the global increase in blindness. To maintain eye health, people have been paying lots of attention to the problem. However, there are still lots of people who have eye problems all around the world. Thus, we proposed the Improved Memetic Direction Exploitation Optimized U-Net Generative Adversarial Network (IMDE-optimized U-Net GAN) model for DR grading. Also, for Multimodal Optimization Problems (MMOPs), a Niching Competition-based Memetic Direction Exploitation (NC-MDE) method is proposed with adaptive local search operations. Additionally, SPatially-ADaptivE (SPADE)-based conditional GANs are used to segment DR. The U-Net GAN model is used to categorize DR images. In the discriminator design, the standard classification network is modified to create the decoder-encoder network known as U-Net. The complete network error of the U-Net GAN architecture is modeled using the IMDE archives. The various performance metrics are used in the method for testing the proposed including accuracy, specificity, sensitivity, and ROC curve. As a result, the proposed method achieves 98.89% of accuracy, 98.54% for sensitivity, and specificity, it achieves 98.67%.