Refining breast cancer classification: Customized attention integration approaches with dense and residual networks for enhanced detection
Objective Breast cancer detection is critical for timely and effective treatment, and automatic detection systems can significantly reduce human error and improve diagnosis speed. This study aims to develop an accurate and robust framework for classifying breast cancer into benign and malignant cate...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-01-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076241309947 |
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| _version_ | 1841556485422710784 |
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| author | Mohammad Sakif Alam Anwar Hossain Efat SM Mahedy Hasan Md Palash Uddin |
| author_facet | Mohammad Sakif Alam Anwar Hossain Efat SM Mahedy Hasan Md Palash Uddin |
| author_sort | Mohammad Sakif Alam |
| collection | DOAJ |
| description | Objective Breast cancer detection is critical for timely and effective treatment, and automatic detection systems can significantly reduce human error and improve diagnosis speed. This study aims to develop an accurate and robust framework for classifying breast cancer into benign and malignant categories using a novel machine learning architecture. Methods We propose a dense-ResNet attention integration (DRAI) architecture that combines DenseNet and ResNet models with three attention mechanisms to enhance feature extraction from the BreakHis dataset. The attention mechanisms focus on regionally important features, improving classification accuracy. A triple-level ensemble (TLE) method combines the performance of multiple models, further enhancing prediction accuracy. Results The proposed DRAI architecture with TLE achieves an accuracy of 99.58% in classifying breast cancer into benign and malignant categories, surpassing existing methodologies. This high accuracy demonstrates the effectiveness of the fusion architecture and its ability to reduce manual errors in breast cancer diagnosis. Conclusion The DRAI architecture with TLE provides a robust, automated framework for breast cancer classification. Its exceptional accuracy lays a solid foundation for future advancements in automated diagnostics and offers a reliable method for aiding early breast cancer detection. |
| format | Article |
| id | doaj-art-3769fbc65b9347e4aa9c1466624dc0d0 |
| institution | Kabale University |
| issn | 2055-2076 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj-art-3769fbc65b9347e4aa9c1466624dc0d02025-01-07T08:03:32ZengSAGE PublishingDigital Health2055-20762025-01-011110.1177/20552076241309947Refining breast cancer classification: Customized attention integration approaches with dense and residual networks for enhanced detectionMohammad Sakif Alam0Anwar Hossain Efat1SM Mahedy Hasan2Md Palash Uddin3 Department of Computer Science and Engineering, , Rajshahi, Bangladesh Department of Computer Science and Engineering, , Dhaka, Bangladesh Department of Computer Science and Engineering, , Rajshahi, Bangladesh Department of Computer Science and Engineering, , Dinajpur, BangladeshObjective Breast cancer detection is critical for timely and effective treatment, and automatic detection systems can significantly reduce human error and improve diagnosis speed. This study aims to develop an accurate and robust framework for classifying breast cancer into benign and malignant categories using a novel machine learning architecture. Methods We propose a dense-ResNet attention integration (DRAI) architecture that combines DenseNet and ResNet models with three attention mechanisms to enhance feature extraction from the BreakHis dataset. The attention mechanisms focus on regionally important features, improving classification accuracy. A triple-level ensemble (TLE) method combines the performance of multiple models, further enhancing prediction accuracy. Results The proposed DRAI architecture with TLE achieves an accuracy of 99.58% in classifying breast cancer into benign and malignant categories, surpassing existing methodologies. This high accuracy demonstrates the effectiveness of the fusion architecture and its ability to reduce manual errors in breast cancer diagnosis. Conclusion The DRAI architecture with TLE provides a robust, automated framework for breast cancer classification. Its exceptional accuracy lays a solid foundation for future advancements in automated diagnostics and offers a reliable method for aiding early breast cancer detection.https://doi.org/10.1177/20552076241309947 |
| spellingShingle | Mohammad Sakif Alam Anwar Hossain Efat SM Mahedy Hasan Md Palash Uddin Refining breast cancer classification: Customized attention integration approaches with dense and residual networks for enhanced detection Digital Health |
| title | Refining breast cancer classification: Customized attention integration approaches with dense and residual networks for enhanced detection |
| title_full | Refining breast cancer classification: Customized attention integration approaches with dense and residual networks for enhanced detection |
| title_fullStr | Refining breast cancer classification: Customized attention integration approaches with dense and residual networks for enhanced detection |
| title_full_unstemmed | Refining breast cancer classification: Customized attention integration approaches with dense and residual networks for enhanced detection |
| title_short | Refining breast cancer classification: Customized attention integration approaches with dense and residual networks for enhanced detection |
| title_sort | refining breast cancer classification customized attention integration approaches with dense and residual networks for enhanced detection |
| url | https://doi.org/10.1177/20552076241309947 |
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