@inproceedings{a5e3aed8dac4448f912612db6306396c,
title = "Parameterising the SA-UNet using a Genetic Algorithm",
abstract = "Deep learning is an excellent way for effectively addressing image processing, and several Neural Networks designs have been explored in this area. The Spatial Attention U-Net architecture, a version of the famous U-Net but which uses DropBlock and an attention block as well as the common U-Net convolutional blocks, is one notable example. Finding the best combination of hyper-parameters is expensive, time consuming and needs expert input. We show the genetic algorithm can be utilized to automatically determine the optimal combination of Spatial Attention U-Net hyper-parameters to train a model to solve a Retinal Blood Vessel Segmentation problem. Our new approach is able to find a model with an accuracy measure of 0.9855, an improvement from our previous experimentation which found a model with accuracy measure of 0.9751. Our new methods exhibit competitive performance with other state-of-the-art Retinal Blood Vessel Segmentation techniques.",
keywords = "Deep Learning, Evolutionary Algorithm, Image Segmentation",
author = "Mahsa Mahdinejad and Aidan Murphy and Patrick Healy and Conor Ryan",
note = "Publisher Copyright: {\textcopyright} 2023 by SCITEPRESS – Science and Technology Publications, Lda.; 14th International Joint Conference on Computational Intelligence, IJCCI 2022 ; Conference date: 24-10-2022 Through 26-10-2022",
year = "2022",
doi = "10.5220/0011528100003332",
language = "English",
isbn = "9789897586118",
series = "International Joint Conference on Computational Intelligence",
publisher = "Science and Technology Publications, Lda",
pages = "97--104",
editor = "Thomas B{\"a}ck and Janusz Kacprzyk and {van Stein}, Niki and Christian Wagner and Jonathan Garibaldi and H.K. Lam and Marie Cottrell and Faiyaz Doctor and Joaquim Filipe and Kevin Warwick",
booktitle = "Proceedings of the 14th International Joint Conference on Computational Intelligence, IJCCI 2022",
}