Enhancing Breast Cancer Diagnosis With Bidirectional Recurrent Neural Networks: A Novel Approach for Histopathological Image Multi-Classification
<p dir="ltr">In recent years, deep learning methods have dramatically improved medical image analysis, though earlier models faced difficulties in capturing intricate spatial and contextual details. These challenges highlighted the necessity for more powerful and flexible models. In...
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2025
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إضافة وسم
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| _version_ | 1864513537995440128 |
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| author | Rajendra Babu Chikkala (22330876) |
| author2 | Chinta Anuradha (22330879) Patnala S. R. Chandra Murty (22330882) S. Rajeswari (3040605) N. Rajeswaran (12558172) M. Murugappan (18842221) Muhammad E. H. Chowdhury (14150526) |
| author2_role | author author author author author author |
| author_facet | Rajendra Babu Chikkala (22330876) Chinta Anuradha (22330879) Patnala S. R. Chandra Murty (22330882) S. Rajeswari (3040605) N. Rajeswaran (12558172) M. Murugappan (18842221) Muhammad E. H. Chowdhury (14150526) |
| author_role | author |
| dc.creator.none.fl_str_mv | Rajendra Babu Chikkala (22330876) Chinta Anuradha (22330879) Patnala S. R. Chandra Murty (22330882) S. Rajeswari (3040605) N. Rajeswaran (12558172) M. Murugappan (18842221) Muhammad E. H. Chowdhury (14150526) |
| dc.date.none.fl_str_mv | 2025-03-12T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2025.3542989 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Enhancing_Breast_Cancer_Diagnosis_With_Bidirectional_Recurrent_Neural_Networks_A_Novel_Approach_for_Histopathological_Image_Multi-Classification/30234514 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Oncology and carcinogenesis Information and computing sciences Machine learning Breast cancer BRNN GRU CNN ResNet FLOP Adagrad optimization algorithm Deep learning Accuracy Transfer learning Medical diagnostic imaging Feature extraction Image analysis Solid modeling Computational modeling Collaboration |
| dc.title.none.fl_str_mv | Enhancing Breast Cancer Diagnosis With Bidirectional Recurrent Neural Networks: A Novel Approach for Histopathological Image Multi-Classification |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">In recent years, deep learning methods have dramatically improved medical image analysis, though earlier models faced difficulties in capturing intricate spatial and contextual details. These challenges highlighted the necessity for more powerful and flexible models. In this study, we introduce an innovative method for the multi-classification of breast cancer histopathological images utilizing Bidirectional Recurrent Neural Networks (BRNN). The BRNN structure consists of four unique elements: the backbone branch for transfer learning, the Gated Recurrent Unit (GRU), the residual collaborative branch, and the feature fusion module. Specifically, the transfer learning aspect exploits a Convolutional Neural Network (CNN) based on the ResNet50 architecture to draw image features from the ImageNet dataset, enhanced with an attention mechanism for improved feature representation. Additionally, the residual branch identifies specific pathological features using Floating Point Operations (FLOP). This cooperative method ensures thorough extraction of breast cancer classification features. The BRNN model was tested on the BreaKHis breast cancer dataset, comprising 7,909 microscopic images across 8 various classes from 82 patients. Our model achieved an average classification accuracy of 97.25 percent, exceeding current leading techniques. The BRNN model, refined using the Adagrad optimization algorithm, efficiently integrates the learned features from both branches. This tool provides oncologists with a substantial enhancement in diagnosing and planning treatment for breast cancer.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3542989" target="_blank">https://dx.doi.org/10.1109/access.2025.3542989</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_a23089cba6bfb4d623abee8a53c92a93 |
| identifier_str_mv | 10.1109/access.2025.3542989 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30234514 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Enhancing Breast Cancer Diagnosis With Bidirectional Recurrent Neural Networks: A Novel Approach for Histopathological Image Multi-ClassificationRajendra Babu Chikkala (22330876)Chinta Anuradha (22330879)Patnala S. R. Chandra Murty (22330882)S. Rajeswari (3040605)N. Rajeswaran (12558172)M. Murugappan (18842221)Muhammad E. H. Chowdhury (14150526)Biomedical and clinical sciencesOncology and carcinogenesisInformation and computing sciencesMachine learningBreast cancerBRNNGRUCNNResNetFLOPAdagrad optimization algorithmDeep learningAccuracyTransfer learningMedical diagnostic imagingFeature extractionImage analysisSolid modelingComputational modelingCollaboration<p dir="ltr">In recent years, deep learning methods have dramatically improved medical image analysis, though earlier models faced difficulties in capturing intricate spatial and contextual details. These challenges highlighted the necessity for more powerful and flexible models. In this study, we introduce an innovative method for the multi-classification of breast cancer histopathological images utilizing Bidirectional Recurrent Neural Networks (BRNN). The BRNN structure consists of four unique elements: the backbone branch for transfer learning, the Gated Recurrent Unit (GRU), the residual collaborative branch, and the feature fusion module. Specifically, the transfer learning aspect exploits a Convolutional Neural Network (CNN) based on the ResNet50 architecture to draw image features from the ImageNet dataset, enhanced with an attention mechanism for improved feature representation. Additionally, the residual branch identifies specific pathological features using Floating Point Operations (FLOP). This cooperative method ensures thorough extraction of breast cancer classification features. The BRNN model was tested on the BreaKHis breast cancer dataset, comprising 7,909 microscopic images across 8 various classes from 82 patients. Our model achieved an average classification accuracy of 97.25 percent, exceeding current leading techniques. The BRNN model, refined using the Adagrad optimization algorithm, efficiently integrates the learned features from both branches. This tool provides oncologists with a substantial enhancement in diagnosing and planning treatment for breast cancer.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3542989" target="_blank">https://dx.doi.org/10.1109/access.2025.3542989</a></p>2025-03-12T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2025.3542989https://figshare.com/articles/journal_contribution/Enhancing_Breast_Cancer_Diagnosis_With_Bidirectional_Recurrent_Neural_Networks_A_Novel_Approach_for_Histopathological_Image_Multi-Classification/30234514CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/302345142025-03-12T12:00:00Z |
| spellingShingle | Enhancing Breast Cancer Diagnosis With Bidirectional Recurrent Neural Networks: A Novel Approach for Histopathological Image Multi-Classification Rajendra Babu Chikkala (22330876) Biomedical and clinical sciences Oncology and carcinogenesis Information and computing sciences Machine learning Breast cancer BRNN GRU CNN ResNet FLOP Adagrad optimization algorithm Deep learning Accuracy Transfer learning Medical diagnostic imaging Feature extraction Image analysis Solid modeling Computational modeling Collaboration |
| status_str | publishedVersion |
| title | Enhancing Breast Cancer Diagnosis With Bidirectional Recurrent Neural Networks: A Novel Approach for Histopathological Image Multi-Classification |
| title_full | Enhancing Breast Cancer Diagnosis With Bidirectional Recurrent Neural Networks: A Novel Approach for Histopathological Image Multi-Classification |
| title_fullStr | Enhancing Breast Cancer Diagnosis With Bidirectional Recurrent Neural Networks: A Novel Approach for Histopathological Image Multi-Classification |
| title_full_unstemmed | Enhancing Breast Cancer Diagnosis With Bidirectional Recurrent Neural Networks: A Novel Approach for Histopathological Image Multi-Classification |
| title_short | Enhancing Breast Cancer Diagnosis With Bidirectional Recurrent Neural Networks: A Novel Approach for Histopathological Image Multi-Classification |
| title_sort | Enhancing Breast Cancer Diagnosis With Bidirectional Recurrent Neural Networks: A Novel Approach for Histopathological Image Multi-Classification |
| topic | Biomedical and clinical sciences Oncology and carcinogenesis Information and computing sciences Machine learning Breast cancer BRNN GRU CNN ResNet FLOP Adagrad optimization algorithm Deep learning Accuracy Transfer learning Medical diagnostic imaging Feature extraction Image analysis Solid modeling Computational modeling Collaboration |