A Study on the Use of Edge TPUs for Eye Fundus Image Segmentation
| Autor: | Francisco Luna-Perejon, Anton Civit, Arturo Morgado-Estevez, José María Rodríguez Corral, Javier Civit-Masot, Manuel Domínguez-Morales |
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| Přispěvatelé: | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores, Universidad de Sevilla. TEP-108: Robótica y Tecnología de Computadores, Ministerio de Ciencia, Innovación y Universidades (MICINN). España |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science Computer Vision and Pattern Recognition (cs.CV) Fundus image Single-board computer ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computer Science - Computer Vision and Pattern Recognition Cloud computing Machine Learning (cs.LG) Image (mathematics) Artificial Intelligence FOS: Electrical engineering electronic engineering information engineering Computer vision Segmentation Electrical and Electronic Engineering business.industry Deep learning Image and Video Processing (eess.IV) Glaucoma Image segmentation Medical image segmentation Electrical Engineering and Systems Science - Image and Video Processing Edge TPU U-Net Control and Systems Engineering Hardware acceleration Enhanced Data Rates for GSM Evolution Artificial intelligence business |
| Popis: | 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 milliseconds per image using Edge TPUs. Preprint of paper published in Engineering Applications of Artificial Intelligence |
| Databáze: | OpenAIRE |
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