A review of lightweight convolutional neural networks for ultrasound signal classification
Ultrasound signal processing plays an important role in medical image analysis. Embedded ultrasonography systems with low power consumption and high portability are suitable for disaster rescue, but due to the difficulty of ultrasonic signal recognition, operators need to have strong professional kn...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2025.1536542/full |
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| author | Bokun Zhang Zhengping Li Yuwen Hao Lijun Wang Xiaoxue Li Yuan Yao |
| author_facet | Bokun Zhang Zhengping Li Yuwen Hao Lijun Wang Xiaoxue Li Yuan Yao |
| author_sort | Bokun Zhang |
| collection | DOAJ |
| description | Ultrasound signal processing plays an important role in medical image analysis. Embedded ultrasonography systems with low power consumption and high portability are suitable for disaster rescue, but due to the difficulty of ultrasonic signal recognition, operators need to have strong professional knowledge, and it is not easy to deploy ultrasonography systems in areas with relatively weak infrastructures. In recent years, with the continuous development in the field of deep learning and artificial intelligence, lightweight convolutional neural networks have brought new opportunities for ultrasound signal processing. This paper focuses on investigating lightweight convolutional neural networks applied to ultrasound signal classification. Combined with the characteristics of ultrasound signals, this paper provides a detailed review of lightweight algorithms from two perspectives: model compression and operational optimization. Among them, model compression deals with the overall framework to reduce network redundancy, and the latter aims at the lightweight design of the basic operational module “convolution” in the network. The experimental results of some classical models and algorithms on the ImageNet dataset are summarized. Through the comprehensive analysis, we present some problems and provide an outlook on the future development of lightweight techniques for ultrasound signal classification. |
| format | Article |
| id | doaj-art-3e366bdd832b4088a2046429ece88e22 |
| institution | OA Journals |
| issn | 1664-042X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj-art-3e366bdd832b4088a2046429ece88e222025-08-20T02:24:54ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2025-04-011610.3389/fphys.2025.15365421536542A review of lightweight convolutional neural networks for ultrasound signal classificationBokun Zhang0Zhengping Li1Yuwen Hao2Lijun Wang3Xiaoxue Li4Yuan Yao5School of Information Science and Technology, North China University of Technology, Beijing, ChinaSchool of Information Science and Technology, North China University of Technology, Beijing, ChinaDisaster Medicine Research Center, Medical Innovation Research Division of the Chinese PLA General Hospital Beijing, China Beijing Key Laboratory of Disaster Medicine, Beijing, ChinaHangzhou Institute of Technology, Xidian University, Xi’an, ChinaHangzhou Institute of Technology, Xidian University, Xi’an, ChinaEmergency Department, 903rd Hospital of PLA Joint Logistic Support Force, Hangzhou, ChinaUltrasound signal processing plays an important role in medical image analysis. Embedded ultrasonography systems with low power consumption and high portability are suitable for disaster rescue, but due to the difficulty of ultrasonic signal recognition, operators need to have strong professional knowledge, and it is not easy to deploy ultrasonography systems in areas with relatively weak infrastructures. In recent years, with the continuous development in the field of deep learning and artificial intelligence, lightweight convolutional neural networks have brought new opportunities for ultrasound signal processing. This paper focuses on investigating lightweight convolutional neural networks applied to ultrasound signal classification. Combined with the characteristics of ultrasound signals, this paper provides a detailed review of lightweight algorithms from two perspectives: model compression and operational optimization. Among them, model compression deals with the overall framework to reduce network redundancy, and the latter aims at the lightweight design of the basic operational module “convolution” in the network. The experimental results of some classical models and algorithms on the ImageNet dataset are summarized. Through the comprehensive analysis, we present some problems and provide an outlook on the future development of lightweight techniques for ultrasound signal classification.https://www.frontiersin.org/articles/10.3389/fphys.2025.1536542/fullultrasoundsignal classificationlightweight technologymodel compressionoptimization of lightweight networkconvolutional neural network |
| spellingShingle | Bokun Zhang Zhengping Li Yuwen Hao Lijun Wang Xiaoxue Li Yuan Yao A review of lightweight convolutional neural networks for ultrasound signal classification Frontiers in Physiology ultrasound signal classification lightweight technology model compression optimization of lightweight network convolutional neural network |
| title | A review of lightweight convolutional neural networks for ultrasound signal classification |
| title_full | A review of lightweight convolutional neural networks for ultrasound signal classification |
| title_fullStr | A review of lightweight convolutional neural networks for ultrasound signal classification |
| title_full_unstemmed | A review of lightweight convolutional neural networks for ultrasound signal classification |
| title_short | A review of lightweight convolutional neural networks for ultrasound signal classification |
| title_sort | review of lightweight convolutional neural networks for ultrasound signal classification |
| topic | ultrasound signal classification lightweight technology model compression optimization of lightweight network convolutional neural network |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2025.1536542/full |
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