Efficient Deep Convolutional Generative Adversarial Network Based on Predictive Modelling for Pandemic health care Diabetes Retinopathy Image Synthesis
Background: Diabetic Retinopathy (DR) is one of the serious complications of diabetes, which can be diagnosed early to prevent vision loss and blindness. Deep learning has progressed rapidly over the past few years, leading to effective image synthesis methods for many medical imaging applications. In the study, we propose an Efficient Deep Convolutional Generative Adversarial Network (EDC-GAN) framework for predicting and generating high-resolution diabetic retinopathy fundus images. Through an attention-based feature extraction method and an improved conditional GAN structure, the proposed model directly synthesizes high-fidelity, diverse, and realistic retinal images. We also employ a hybrid loss function composed of perceptual loss and adversarial loss to improve the quality of synthetic images even more. Large-scale contrastive learning with an instance noise module is also proposed to retain fine-grained details in the synthesized images, as well as a new component, Feature-Preserving Adaptive Normalization (FPAN), in the model. We train the model on publicly available DR datasets and validate it using several evaluation metrics such as Freshet Inception Distance (FID), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR). Experimental results show that EDC-GAN outperforms existing image synthesis methods in terms of generating higher quality and more realistic synthetic fundus images and improving the modeling performance in terms of predictive power. Therefore, the proposed model presents a very effective approach to augment a scarce DR dataset and to assure robust computer-aided diagnosis (CAD) for DR detection.