LightEyes: A Lightweight Fundus Segmentation Network for Mobile Edge Computing
Fundus is the only structure that can be observed without trauma to the human body. By analyzing color fundus images, the diagnosis basis for various diseases can be obtained. Recently, fundus image segmentation has witnessed vast progress with the development of deep learning. However, the improvem...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/9/3112 |
| _version_ | 1797502977875902464 |
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| author | Song Guo |
| author_facet | Song Guo |
| author_sort | Song Guo |
| collection | DOAJ |
| description | Fundus is the only structure that can be observed without trauma to the human body. By analyzing color fundus images, the diagnosis basis for various diseases can be obtained. Recently, fundus image segmentation has witnessed vast progress with the development of deep learning. However, the improvement of segmentation accuracy comes with the complexity of deep models. As a result, these models show low inference speeds and high memory usages when deploying to mobile edges. To promote the deployment of deep fundus segmentation models to mobile devices, we aim to design a lightweight fundus segmentation network. Our observation comes from the fact that high-resolution representations could boost the segmentation of tiny fundus structures, and the classification of small fundus structures depends more on local features. To this end, we propose a lightweight segmentation model called LightEyes. We first design a high-resolution backbone network to learn high-resolution representations, so that the spatial relationship between feature maps can be always retained. Meanwhile, considering high-resolution features means high memory usage; for each layer, we use at most 16 convolutional filters to reduce memory usage and decrease training difficulty. LightEyes has been verified on three kinds of fundus segmentation tasks, including the hard exudate, the microaneurysm, and the vessel, on five publicly available datasets. Experimental results show that LightEyes achieves highly competitive segmentation accuracy and segmentation speed compared with state-of-the-art fundus segmentation models, while running at 1.6 images/s Cambricon-1A speed and 51.3 images/s GPU speed with only 36k parameters. |
| first_indexed | 2024-03-10T03:43:54Z |
| format | Article |
| id | doaj.art-65a935f8a6cc4a96a4a0a7b769ef5649 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:43:54Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-65a935f8a6cc4a96a4a0a7b769ef56492023-11-23T09:13:37ZengMDPI AGSensors1424-82202022-04-01229311210.3390/s22093112LightEyes: A Lightweight Fundus Segmentation Network for Mobile Edge ComputingSong Guo0School of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaFundus is the only structure that can be observed without trauma to the human body. By analyzing color fundus images, the diagnosis basis for various diseases can be obtained. Recently, fundus image segmentation has witnessed vast progress with the development of deep learning. However, the improvement of segmentation accuracy comes with the complexity of deep models. As a result, these models show low inference speeds and high memory usages when deploying to mobile edges. To promote the deployment of deep fundus segmentation models to mobile devices, we aim to design a lightweight fundus segmentation network. Our observation comes from the fact that high-resolution representations could boost the segmentation of tiny fundus structures, and the classification of small fundus structures depends more on local features. To this end, we propose a lightweight segmentation model called LightEyes. We first design a high-resolution backbone network to learn high-resolution representations, so that the spatial relationship between feature maps can be always retained. Meanwhile, considering high-resolution features means high memory usage; for each layer, we use at most 16 convolutional filters to reduce memory usage and decrease training difficulty. LightEyes has been verified on three kinds of fundus segmentation tasks, including the hard exudate, the microaneurysm, and the vessel, on five publicly available datasets. Experimental results show that LightEyes achieves highly competitive segmentation accuracy and segmentation speed compared with state-of-the-art fundus segmentation models, while running at 1.6 images/s Cambricon-1A speed and 51.3 images/s GPU speed with only 36k parameters.https://www.mdpi.com/1424-8220/22/9/3112lightweight networkfast semantic segmentationmobile edge computingfundus image |
| spellingShingle | Song Guo LightEyes: A Lightweight Fundus Segmentation Network for Mobile Edge Computing Sensors lightweight network fast semantic segmentation mobile edge computing fundus image |
| title | LightEyes: A Lightweight Fundus Segmentation Network for Mobile Edge Computing |
| title_full | LightEyes: A Lightweight Fundus Segmentation Network for Mobile Edge Computing |
| title_fullStr | LightEyes: A Lightweight Fundus Segmentation Network for Mobile Edge Computing |
| title_full_unstemmed | LightEyes: A Lightweight Fundus Segmentation Network for Mobile Edge Computing |
| title_short | LightEyes: A Lightweight Fundus Segmentation Network for Mobile Edge Computing |
| title_sort | lighteyes a lightweight fundus segmentation network for mobile edge computing |
| topic | lightweight network fast semantic segmentation mobile edge computing fundus image |
| url | https://www.mdpi.com/1424-8220/22/9/3112 |
| work_keys_str_mv | AT songguo lighteyesalightweightfundussegmentationnetworkformobileedgecomputing |