Medical images synthesis of diabetic retinopathy using GANS

Abstract

The scarcity of annotated medical images is a significant challenge in training effective machine learning models for diagnosing diabetic retinopathy, a leading cause of blindness. Acquiring high-quality retinal images is costly and time-consuming, and a lack of diversity in existing datasets raises the challenge even further. Such limitations make it challenging to come up with reliable diagnostic tools that depend on comprehensive and varied data for effective training. In order to generate real images for the different diabetic retinopathy stages, this work uses GANs. Therefore, such an issue could be addressed by just incorporating those realistic images into current datasets that increase both size and variability as well as enhance diagnostic models. Furthermore, the generated data serves as a valuable asset for exploring disease patterns that are typically underrepresented in existing datasets, offering a promising approach to advance diabetic retinopathy diagnosis.