Academic Journal
A segmentation-combination data augmentation strategy and dual attention mechanism for accurate Chinese herbal medicine microscopic identification.
| Title: | A segmentation-combination data augmentation strategy and dual attention mechanism for accurate Chinese herbal medicine microscopic identification. |
|---|---|
| Authors: | Zhu, Xiaoying, Pang, Guangyao, He, Xi, Chen, Yue, Yu, Zhenming |
| Source: | Frontiers in Plant Science; 2024, p1-15, 15p |
| Subject Terms: | Herbal medicine, Data augmentation, Chinese medicine, Process capability, Cellular recognition, Deep learning |
| Abstract: | Introduction: Chinese Herbal Medicine (CHM), with its deep-rooted history and increasing global recognition, encounters significant challenges in automation for microscopic identification. These challenges stem from limitations in traditional microscopic methods, scarcity of publicly accessible datasets, imbalanced class distributions, and issues with small, unevenly distributed, incomplete, or blurred features in microscopic images. Methods: To address these challenges, this study proposes a novel deep learning-based approach for Chinese Herbal Medicine Microscopic Identification (CHMMI). A segmentation-combination data augmentation strategy is employed to expand and balance datasets, capturing comprehensive feature sets. Additionally, a shallow-deep dual attention module enhances the model's ability to focus on relevant features across different layers. Multi-scale inference is integrated to process features at various scales effectively, improving the accuracy of object detection and identification. Results: The CHMMI approach achieved an Average Precision (AP) of 0.841, a mean Average Precision at IoU=.50 (mAP@.5) of 0.887, a mean Average Precision at IoU from.50 to.95 (mAP@.5:.95) of 0.551, and a Matthews Correlation Coefficient of 0.898. These results demonstrate superior performance compared to state-of-the-art methods, including YOLOv5, SSD, Faster R-CNN, and ResNet. Discussion: The proposed CHMMI approach addresses key limitations of traditional methods, offering a robust solution for automating CHM microscopic identification. Its high accuracy and effective feature processing capabilities underscore its potential to modernize and support the growth of the CHM industry. [ABSTRACT FROM AUTHOR] |
| Copyright of Frontiers in Plant Science is the property of Frontiers Media S.A. 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: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=1664462X&ISBN=&volume=&issue=&date=20241212&spage=1&pages=1-15&title=Frontiers in Plant Science&atitle=A%20segmentation-combination%20data%20augmentation%20strategy%20and%20dual%20attention%20mechanism%20for%20accurate%20Chinese%20herbal%20medicine%20microscopic%20identification.&aulast=Zhu%2C%20Xiaoying&id=DOI:10.3389/fpls.2024.1442968 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
|---|---|
| Header | DbId: edb DbLabel: Complementary Index An: 181605954 RelevancyScore: 983 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 983.441528320313 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: A segmentation-combination data augmentation strategy and dual attention mechanism for accurate Chinese herbal medicine microscopic identification. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhu%2C+Xiaoying%22">Zhu, Xiaoying</searchLink><br /><searchLink fieldCode="AR" term="%22Pang%2C+Guangyao%22">Pang, Guangyao</searchLink><br /><searchLink fieldCode="AR" term="%22He%2C+Xi%22">He, Xi</searchLink><br /><searchLink fieldCode="AR" term="%22Chen%2C+Yue%22">Chen, Yue</searchLink><br /><searchLink fieldCode="AR" term="%22Yu%2C+Zhenming%22">Yu, Zhenming</searchLink> – Name: TitleSource Label: Source Group: Src Data: Frontiers in Plant Science; 2024, p1-15, 15p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Herbal+medicine%22">Herbal medicine</searchLink><br /><searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Chinese+medicine%22">Chinese medicine</searchLink><br /><searchLink fieldCode="DE" term="%22Process+capability%22">Process capability</searchLink><br /><searchLink fieldCode="DE" term="%22Cellular+recognition%22">Cellular recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Introduction: Chinese Herbal Medicine (CHM), with its deep-rooted history and increasing global recognition, encounters significant challenges in automation for microscopic identification. These challenges stem from limitations in traditional microscopic methods, scarcity of publicly accessible datasets, imbalanced class distributions, and issues with small, unevenly distributed, incomplete, or blurred features in microscopic images. Methods: To address these challenges, this study proposes a novel deep learning-based approach for Chinese Herbal Medicine Microscopic Identification (CHMMI). A segmentation-combination data augmentation strategy is employed to expand and balance datasets, capturing comprehensive feature sets. Additionally, a shallow-deep dual attention module enhances the model's ability to focus on relevant features across different layers. Multi-scale inference is integrated to process features at various scales effectively, improving the accuracy of object detection and identification. Results: The CHMMI approach achieved an Average Precision (AP) of 0.841, a mean Average Precision at IoU=.50 (mAP@.5) of 0.887, a mean Average Precision at IoU from.50 to.95 (mAP@.5:.95) of 0.551, and a Matthews Correlation Coefficient of 0.898. These results demonstrate superior performance compared to state-of-the-art methods, including YOLOv5, SSD, Faster R-CNN, and ResNet. Discussion: The proposed CHMMI approach addresses key limitations of traditional methods, offering a robust solution for automating CHM microscopic identification. Its high accuracy and effective feature processing capabilities underscore its potential to modernize and support the growth of the CHM industry. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Frontiers in Plant Science is the property of Frontiers Media S.A. 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=edb&AN=181605954 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3389/fpls.2024.1442968 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1 Subjects: – SubjectFull: Herbal medicine Type: general – SubjectFull: Data augmentation Type: general – SubjectFull: Chinese medicine Type: general – SubjectFull: Process capability Type: general – SubjectFull: Cellular recognition Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: A segmentation-combination data augmentation strategy and dual attention mechanism for accurate Chinese herbal medicine microscopic identification. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhu, Xiaoying – PersonEntity: Name: NameFull: Pang, Guangyao – PersonEntity: Name: NameFull: He, Xi – PersonEntity: Name: NameFull: Chen, Yue – PersonEntity: Name: NameFull: Yu, Zhenming IsPartOfRelationships: – BibEntity: Dates: – D: 12 M: 12 Text: 2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 1664462X Titles: – TitleFull: Frontiers in Plant Science Type: main |
| ResultId | 1 |