Application of medical clinic system using improved neural network-based image segmentation technique.
Saved in:
| Title: | Application of medical clinic system using improved neural network-based image segmentation technique. |
|---|---|
| Authors: | Wu, Fenglang1 (AUTHOR) wufenglang@xjtufh.edu.cn, Liu, Xinran1 (AUTHOR), Wang, Yudan1 (AUTHOR), Li, Xiaoliang1 (AUTHOR), Zhou, Ming1 (AUTHOR) |
| Source: | Neural Computing & Applications. May2025, Vol. 37 Issue 13, p8193-8203. 11p. |
| Subjects: | Artificial intelligence, Database design, Information storage & retrieval systems, Image processing, Systems design |
| Abstract: | In the design of medical clinical systems, the segmentation and recognition of medical images are important factors affecting the usability effect of the system. Further research and analysis are needed to improve the segmentation effect of medical images by improving neural network technology. In this paper, a new medical image segmentation technique is designed by improving neural networks and introducing an attention mechanism. After the medical image is convoluted, the image feature information enters the channel attention mechanism module. Each feature channel has a weight. The larger the weight, the greater the correlation between the feature and the channel. The smaller the weight, the smaller the correlation between the feature and the channel. This paper analyses the system design objectives, analyses the requirements of the medical clinical system, and designs the software architecture composed of the client layer, the data layer, and the application. For example, the network architecture is mainly composed of the client and the server. The functional modules mainly include six modules: user management module, department use module, equipment maintenance module, budget management module, asset management module, and medical equipment regulations module. Lastly, the choice of hardware and the design of the database for the medical clinical system are explained in detail. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 184671160 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Application of medical clinic system using improved neural network-based image segmentation technique. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wu%2C+Fenglang%22">Wu, Fenglang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wufenglang@xjtufh.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Xinran%22">Liu, Xinran</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Yudan%22">Wang, Yudan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Xiaoliang%22">Li, Xiaoliang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Ming%22">Zhou, Ming</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. May2025, Vol. 37 Issue 13, p8193-8203. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Database+design%22">Database design</searchLink><br /><searchLink fieldCode="DE" term="%22Information+storage+%26+retrieval+systems%22">Information storage & retrieval systems</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Systems+design%22">Systems design</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In the design of medical clinical systems, the segmentation and recognition of medical images are important factors affecting the usability effect of the system. Further research and analysis are needed to improve the segmentation effect of medical images by improving neural network technology. In this paper, a new medical image segmentation technique is designed by improving neural networks and introducing an attention mechanism. After the medical image is convoluted, the image feature information enters the channel attention mechanism module. Each feature channel has a weight. The larger the weight, the greater the correlation between the feature and the channel. The smaller the weight, the smaller the correlation between the feature and the channel. This paper analyses the system design objectives, analyses the requirements of the medical clinical system, and designs the software architecture composed of the client layer, the data layer, and the application. For example, the network architecture is mainly composed of the client and the server. The functional modules mainly include six modules: user management module, department use module, equipment maintenance module, budget management module, asset management module, and medical equipment regulations module. Lastly, the choice of hardware and the design of the database for the medical clinical system are explained in detail. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=184671160 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00521-022-07913-y Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 8193 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Database design Type: general – SubjectFull: Information storage & retrieval systems Type: general – SubjectFull: Image processing Type: general – SubjectFull: Systems design Type: general Titles: – TitleFull: Application of medical clinic system using improved neural network-based image segmentation technique. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wu, Fenglang – PersonEntity: Name: NameFull: Liu, Xinran – PersonEntity: Name: NameFull: Wang, Yudan – PersonEntity: Name: NameFull: Li, Xiaoliang – PersonEntity: Name: NameFull: Zhou, Ming IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09410643 Numbering: – Type: volume Value: 37 – Type: issue Value: 13 Titles: – TitleFull: Neural Computing & Applications Type: main |
| ResultId | 1 |
