GA-based U-Net architecture optimization applied to retina blood vessel segmentation

Blood vessel extraction in digital retinal images is an important step in medical image analysis for abnormality detection and also obtaining good retinopathy diabetic diagnosis; this is often referred to as the Retinal Blood Vessel Segmentation task and current state-of-the-art approaches all use some form of neural networks. Designing neural network architecture and selecting appropriate hyper-parameters for a specific task is challenging. In recent works, increasingly more complex models are starting to appear, but in this work, we present a simple and small model with a very low number of parameters with good performance compared with the state of the art algorithms. In particular, we choose a standard Genetic Algorithm (GA) for selecting the parameters of the model and we use an expert-designed U-net based model, which has become a very popular tool in image segmentation problems. Experimental results show that GA is able to find a much shorter architecture and acceptable accuracy compared to the U-net manually designed. This finding puts on the right track to be able in the future to implement these models in portable applications