Multi-Scale Vision Transformer with Optimized Feature Fusion for Mammographic Breast Cancer Classification
<b>Background</b>: Breast cancer remains one of the leading causes of mortality among women worldwide, highlighting the critical need for accurate and efficient diagnostic methods. <b>Methods</b>: Traditional deep learning models often struggle with feature redundancy, subopt...
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MDPI AG
2025-05-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/15/11/1361 |
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| author | Soaad Ahmed Naira Elazab Mostafa M. El-Gayar Mohammed Elmogy Yasser M. Fouda |
| author_facet | Soaad Ahmed Naira Elazab Mostafa M. El-Gayar Mohammed Elmogy Yasser M. Fouda |
| author_sort | Soaad Ahmed |
| collection | DOAJ |
| description | <b>Background</b>: Breast cancer remains one of the leading causes of mortality among women worldwide, highlighting the critical need for accurate and efficient diagnostic methods. <b>Methods</b>: Traditional deep learning models often struggle with feature redundancy, suboptimal feature fusion, and inefficient selection of discriminative features, leading to limitations in classification performance. To address these challenges, we propose a new deep learning framework that leverages MAX-ViT for multi-scale feature extraction, ensuring robust and hierarchical representation learning. A gated attention fusion module (GAFM) is introduced to dynamically integrate the extracted features, enhancing the discriminative power of the fused representation. Additionally, we employ Harris Hawks optimization (HHO) for feature selection, reducing redundancy and improving classification efficiency. Finally, XGBoost is utilized for classification, taking advantage of its strong generalization capabilities. <b>Results</b>: We evaluate our model on the King Abdulaziz University Mammogram Dataset, categorized based on BI-RADS classifications. Experimental results demonstrate the effectiveness of our approach, achieving 98.2% for accuracy, 98.0% for precision, 98.1% for recall, 98.0% for F1-score, 98.9% for the area under the curve (AUC), and 95% for the Matthews correlation coefficient (MCC), outperforming existing state-of-the-art models. <b>Conclusions</b>: These results validate the robustness of our fusion-based framework in improving breast cancer diagnosis and classification. |
| format | Article |
| id | doaj-art-b6476d1322bd4cf0b38437ed1cb2a1bc |
| institution | OA Journals |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-b6476d1322bd4cf0b38437ed1cb2a1bc2025-08-20T02:23:08ZengMDPI AGDiagnostics2075-44182025-05-011511136110.3390/diagnostics15111361Multi-Scale Vision Transformer with Optimized Feature Fusion for Mammographic Breast Cancer ClassificationSoaad Ahmed0Naira Elazab1Mostafa M. El-Gayar2Mohammed Elmogy3Yasser M. Fouda4Computer Science Division, Mathematics Department, Faculty of Science, Mansoura University, Mansoura 35516, EgyptInformation Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, EgyptInformation Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, EgyptInformation Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, EgyptComputer Science Division, Mathematics Department, Faculty of Science, Mansoura University, Mansoura 35516, Egypt<b>Background</b>: Breast cancer remains one of the leading causes of mortality among women worldwide, highlighting the critical need for accurate and efficient diagnostic methods. <b>Methods</b>: Traditional deep learning models often struggle with feature redundancy, suboptimal feature fusion, and inefficient selection of discriminative features, leading to limitations in classification performance. To address these challenges, we propose a new deep learning framework that leverages MAX-ViT for multi-scale feature extraction, ensuring robust and hierarchical representation learning. A gated attention fusion module (GAFM) is introduced to dynamically integrate the extracted features, enhancing the discriminative power of the fused representation. Additionally, we employ Harris Hawks optimization (HHO) for feature selection, reducing redundancy and improving classification efficiency. Finally, XGBoost is utilized for classification, taking advantage of its strong generalization capabilities. <b>Results</b>: We evaluate our model on the King Abdulaziz University Mammogram Dataset, categorized based on BI-RADS classifications. Experimental results demonstrate the effectiveness of our approach, achieving 98.2% for accuracy, 98.0% for precision, 98.1% for recall, 98.0% for F1-score, 98.9% for the area under the curve (AUC), and 95% for the Matthews correlation coefficient (MCC), outperforming existing state-of-the-art models. <b>Conclusions</b>: These results validate the robustness of our fusion-based framework in improving breast cancer diagnosis and classification.https://www.mdpi.com/2075-4418/15/11/1361breast cancer classificationMAX-ViTgated attention fusion module (GAFM)Harris Hawks optimization (HHO)mammography analysis |
| spellingShingle | Soaad Ahmed Naira Elazab Mostafa M. El-Gayar Mohammed Elmogy Yasser M. Fouda Multi-Scale Vision Transformer with Optimized Feature Fusion for Mammographic Breast Cancer Classification Diagnostics breast cancer classification MAX-ViT gated attention fusion module (GAFM) Harris Hawks optimization (HHO) mammography analysis |
| title | Multi-Scale Vision Transformer with Optimized Feature Fusion for Mammographic Breast Cancer Classification |
| title_full | Multi-Scale Vision Transformer with Optimized Feature Fusion for Mammographic Breast Cancer Classification |
| title_fullStr | Multi-Scale Vision Transformer with Optimized Feature Fusion for Mammographic Breast Cancer Classification |
| title_full_unstemmed | Multi-Scale Vision Transformer with Optimized Feature Fusion for Mammographic Breast Cancer Classification |
| title_short | Multi-Scale Vision Transformer with Optimized Feature Fusion for Mammographic Breast Cancer Classification |
| title_sort | multi scale vision transformer with optimized feature fusion for mammographic breast cancer classification |
| topic | breast cancer classification MAX-ViT gated attention fusion module (GAFM) Harris Hawks optimization (HHO) mammography analysis |
| url | https://www.mdpi.com/2075-4418/15/11/1361 |
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