dc.creator | Civit Masot, Javier | es |
dc.creator | Luna Perejón, Francisco | es |
dc.creator | Rodríguez Corral, José María | es |
dc.creator | Domínguez Morales, Manuel Jesús | es |
dc.creator | Morgado Estévez, Arturo | es |
dc.creator | Civit Balcells, Antón | es |
dc.date.accessioned | 2022-07-20T08:19:21Z | |
dc.date.available | 2022-07-20T08:19:21Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Civit Masot, J., Luna Perejón, F., Rodríguez Corral, J.M., Domínguez Morales, M.J., Morgado Estévez, A. y Civit Balcells, A. (2021). A study on the use of Edge TPUs for eye fundus image segmentation. Engineering Applications of Artificial Intelligence, 104 (September 2021, art. nº104384) | |
dc.identifier.issn | 0952-1976 | es |
dc.identifier.uri | https://hdl.handle.net/11441/135624 | |
dc.description.abstract | Medical image segmentation can be implemented using Deep Learning methods with fast and efficient
segmentation networks. Single-board computers (SBCs) are difficult to use to train deep networks due to their
memory and processing limitations. Specific hardware such as Google’s Edge TPU makes them suitable for real
time predictions using complex pre-trained networks. In this work, we study the performance of two SBCs, with
and without hardware acceleration for fundus image segmentation, though the conclusions of this study can be
applied to the segmentation by deep neural networks of other types of medical images. To test the benefits of
hardware acceleration, we use networks and datasets from a previous published work and generalize them by
testing with a dataset with ultrasound thyroid images. We measure prediction times in both SBCs and compare
them with a cloud based TPU system. The results show the feasibility of Machine Learning accelerated SBCs
for optic disc and cup segmentation obtaining times below 25 ms per image using Edge TPUs. | es |
dc.description.sponsorship | Ministerio de Ciencia, Innovación y Universidades EQC2018-005190-P | es |
dc.format | application/pdf | es |
dc.format.extent | 8 | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Engineering Applications of Artificial Intelligence, 104 (September 2021, art. nº104384) | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Deep learning | es |
dc.subject | Edge TPU | es |
dc.subject | Medical image segmentation | es |
dc.subject | Glaucoma | es |
dc.subject | Single-board computer | es |
dc.subject | U-Net | es |
dc.title | A study on the use of Edge TPUs for eye fundus image segmentation | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores | es |
dc.relation.projectID | EQC2018-005190-P | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0952197621002323?via%3Dihub | es |
dc.identifier.doi | 10.1016/j.engappai.2021.104384 | es |
dc.contributor.group | Universidad de Sevilla. TEP-108: Robótica y Tecnología de Computadores | es |
dc.journaltitle | Engineering Applications of Artificial Intelligence | es |
dc.publication.volumen | 104 | es |
dc.publication.issue | September 2021, art. nº104384 | es |
dc.contributor.funder | Ministerio de Ciencia, Innovación y Universidades (MICINN). España | es |