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Enhancing Medical Image Classification With Context Modulated Attention and Multi-Scale Feature Fusion

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Title: Enhancing Medical Image Classification With Context Modulated Attention and Multi-Scale Feature Fusion
Authors: Renhan Zhang, Xuegang Luo, Junrui Lv, Junyang Cao, Yangping Zhu, Juan Wang, Bochuan Zheng
Source: IEEE Access, Vol 13, Pp 15226-15243 (2025)
Publisher Information: IEEE, 2025.
Publication Year: 2025
Collection: LCC:Electrical engineering. Electronics. Nuclear engineering
Subject Terms: Medical images, global semantics, local features, transformer, context modulated attention, multi-stage feature fusion network, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
More Details: This research proposes a multi-stage feature fusion network (MSFF) for medical image classification. In view of the problems existing in medical images, such as noise, diversity, and similarity among different classes, MSFF enhances the global context perception in the window partitioning framework through Context Modulation Attention (CMA). Meanwhile, it extracts fine-grained local information via the multi-stage Contextual Information Refinement (CIR) module and gradually fuses multi-stage local and global features to generate richer semantic representations. The experimental results demonstrate that MSFF significantly outperforms existing methods in multiple performance metrics (including accuracy, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), Kappa coefficient, Area Under the Curve (AUC), balanced accuracy, and geometric mean) on four datasets (Endoscopic Bladder Tissue, Kvasir, SARS-COV-2 Ct-Scan, and Thyroid Nodule), showing its excellent performance in the task of medical image classification.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2169-3536
Relation: https://ieeexplore.ieee.org/document/10848071/; https://doaj.org/toc/2169-3536
DOI: 10.1109/ACCESS.2025.3532354
Access URL: https://doaj.org/article/bbe3289c856f409f80e1ae37657b70e4
Accession Number: edsdoj.bbe3289c856f409f80e1ae37657b70e4
Database: Directory of Open Access Journals
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  Data: Enhancing Medical Image Classification With Context Modulated Attention and Multi-Scale Feature Fusion
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  Data: <searchLink fieldCode="AR" term="%22Renhan+Zhang%22">Renhan Zhang</searchLink><br /><searchLink fieldCode="AR" term="%22Xuegang+Luo%22">Xuegang Luo</searchLink><br /><searchLink fieldCode="AR" term="%22Junrui+Lv%22">Junrui Lv</searchLink><br /><searchLink fieldCode="AR" term="%22Junyang+Cao%22">Junyang Cao</searchLink><br /><searchLink fieldCode="AR" term="%22Yangping+Zhu%22">Yangping Zhu</searchLink><br /><searchLink fieldCode="AR" term="%22Juan+Wang%22">Juan Wang</searchLink><br /><searchLink fieldCode="AR" term="%22Bochuan+Zheng%22">Bochuan Zheng</searchLink>
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  Data: IEEE Access, Vol 13, Pp 15226-15243 (2025)
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  Data: LCC:Electrical engineering. Electronics. Nuclear engineering
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  Data: <searchLink fieldCode="DE" term="%22Medical+images%22">Medical images</searchLink><br /><searchLink fieldCode="DE" term="%22global+semantics%22">global semantics</searchLink><br /><searchLink fieldCode="DE" term="%22local+features%22">local features</searchLink><br /><searchLink fieldCode="DE" term="%22transformer%22">transformer</searchLink><br /><searchLink fieldCode="DE" term="%22context+modulated+attention%22">context modulated attention</searchLink><br /><searchLink fieldCode="DE" term="%22multi-stage+feature+fusion+network%22">multi-stage feature fusion network</searchLink><br /><searchLink fieldCode="DE" term="%22Electrical+engineering%2E+Electronics%2E+Nuclear+engineering%22">Electrical engineering. Electronics. Nuclear engineering</searchLink><br /><searchLink fieldCode="DE" term="%22TK1-9971%22">TK1-9971</searchLink>
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  Data: This research proposes a multi-stage feature fusion network (MSFF) for medical image classification. In view of the problems existing in medical images, such as noise, diversity, and similarity among different classes, MSFF enhances the global context perception in the window partitioning framework through Context Modulation Attention (CMA). Meanwhile, it extracts fine-grained local information via the multi-stage Contextual Information Refinement (CIR) module and gradually fuses multi-stage local and global features to generate richer semantic representations. The experimental results demonstrate that MSFF significantly outperforms existing methods in multiple performance metrics (including accuracy, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), Kappa coefficient, Area Under the Curve (AUC), balanced accuracy, and geometric mean) on four datasets (Endoscopic Bladder Tissue, Kvasir, SARS-COV-2 Ct-Scan, and Thyroid Nodule), showing its excellent performance in the task of medical image classification.
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      – SubjectFull: global semantics
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      – SubjectFull: local features
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      – SubjectFull: context modulated attention
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      – SubjectFull: TK1-9971
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