EnSLDe: an enhanced short-range and long-range dependent system for brain tumor classification
IntroductionBrain tumors pose significant harm to the functionality of the human nervous system. There are lots of models which can classify brain tumor type. However, the available methods did not pay special attention to long-range information, which limits model accuracy improvement.MethodsTo sol...
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| Format: | Article |
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1512739/full |
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| author | Wenna Chen Junqiang Liu Xinghua Tan Jincan Zhang Ganqin Du Qizhi Fu Hongwei Jiang |
| author_facet | Wenna Chen Junqiang Liu Xinghua Tan Jincan Zhang Ganqin Du Qizhi Fu Hongwei Jiang |
| author_sort | Wenna Chen |
| collection | DOAJ |
| description | IntroductionBrain tumors pose significant harm to the functionality of the human nervous system. There are lots of models which can classify brain tumor type. However, the available methods did not pay special attention to long-range information, which limits model accuracy improvement.MethodsTo solve this problem, in this paper, an enhanced short-range and long-range dependent system for brain tumor classification, named as EnSLDe, is proposed. The EnSLDe model consists of three main modules: the Feature Extraction Module (FExM), the Feature Enhancement Module (FEnM), and the Classification Module. Firstly, the FExM is used to extract features and the multi-scale parallel subnetwork is constructed to fuse shallow and deep features. Then, the extracted features are enhanced by the FEnM. The FEnM can capture the important dependencies across a larger sequence range and retain critical information at a local scale. Finally, the fused and enhanced features are input to the classification module for brain tumor classification. The combination of these modules enables the efficient extraction of both local and global contextual information.ResultsIn order to validate the model, two public data sets including glioma, meningioma, and pituitary tumor were validated, and good experimental results were obtained, demonstrating the potential of the model EnSLDe in brain tumor classification. |
| format | Article |
| id | doaj-art-1e59e2f36a1a4d3cbec93e276562984b |
| institution | DOAJ |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-1e59e2f36a1a4d3cbec93e276562984b2025-08-20T03:08:47ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-04-011510.3389/fonc.2025.15127391512739EnSLDe: an enhanced short-range and long-range dependent system for brain tumor classificationWenna Chen0Junqiang Liu1Xinghua Tan2Jincan Zhang3Ganqin Du4Qizhi Fu5Hongwei Jiang6The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, ChinaCollege of Information Engineering, Henan University of Science and Technology, Luoyang, ChinaCollege of Information Engineering, Henan University of Science and Technology, Luoyang, ChinaCollege of Information Engineering, Henan University of Science and Technology, Luoyang, ChinaThe First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, ChinaThe First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, ChinaThe First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, ChinaIntroductionBrain tumors pose significant harm to the functionality of the human nervous system. There are lots of models which can classify brain tumor type. However, the available methods did not pay special attention to long-range information, which limits model accuracy improvement.MethodsTo solve this problem, in this paper, an enhanced short-range and long-range dependent system for brain tumor classification, named as EnSLDe, is proposed. The EnSLDe model consists of three main modules: the Feature Extraction Module (FExM), the Feature Enhancement Module (FEnM), and the Classification Module. Firstly, the FExM is used to extract features and the multi-scale parallel subnetwork is constructed to fuse shallow and deep features. Then, the extracted features are enhanced by the FEnM. The FEnM can capture the important dependencies across a larger sequence range and retain critical information at a local scale. Finally, the fused and enhanced features are input to the classification module for brain tumor classification. The combination of these modules enables the efficient extraction of both local and global contextual information.ResultsIn order to validate the model, two public data sets including glioma, meningioma, and pituitary tumor were validated, and good experimental results were obtained, demonstrating the potential of the model EnSLDe in brain tumor classification.https://www.frontiersin.org/articles/10.3389/fonc.2025.1512739/fullbrain tumor classificationfeature extractionfeature enhancementlong-range dependenciesattention |
| spellingShingle | Wenna Chen Junqiang Liu Xinghua Tan Jincan Zhang Ganqin Du Qizhi Fu Hongwei Jiang EnSLDe: an enhanced short-range and long-range dependent system for brain tumor classification Frontiers in Oncology brain tumor classification feature extraction feature enhancement long-range dependencies attention |
| title | EnSLDe: an enhanced short-range and long-range dependent system for brain tumor classification |
| title_full | EnSLDe: an enhanced short-range and long-range dependent system for brain tumor classification |
| title_fullStr | EnSLDe: an enhanced short-range and long-range dependent system for brain tumor classification |
| title_full_unstemmed | EnSLDe: an enhanced short-range and long-range dependent system for brain tumor classification |
| title_short | EnSLDe: an enhanced short-range and long-range dependent system for brain tumor classification |
| title_sort | enslde an enhanced short range and long range dependent system for brain tumor classification |
| topic | brain tumor classification feature extraction feature enhancement long-range dependencies attention |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1512739/full |
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