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

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conference contribution
posted on 2022-11-29, 15:24 authored by Vipul Popat, Mahsa MahdinejadMahsa Mahdinejad, Oscar S. Dalmau Cedeno, ENRIQUE NAREDOENRIQUE NAREDO, Conor RyanConor Ryan

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 


SFI Centre for Research Training in Artificial Intelligence

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Automatic Design of Digital Circuits (ADDC)

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n Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020), pp. 192-199



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