GGLA-NeXtE2NET: A Dual-Branch Ensemble Network With Gated Global-Local Attention for Enhanced Brain Tumor Recognition
Due to the limited availability of training data, the diverse shapes of brain tumors among different patients, inter-class similarity, and intra-class variation, achieving high recognition accuracy and speed in deep learning-based brain tumor recognition remains challenging. To address these issues,...
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IEEE
2025-01-01
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| author | Adnan Saeed Khurram Shehzad Shahzad Sarwar Bhatti Saim Ahmed Ahmad Taher Azar |
| author_facet | Adnan Saeed Khurram Shehzad Shahzad Sarwar Bhatti Saim Ahmed Ahmad Taher Azar |
| author_sort | Adnan Saeed |
| collection | DOAJ |
| description | Due to the limited availability of training data, the diverse shapes of brain tumors among different patients, inter-class similarity, and intra-class variation, achieving high recognition accuracy and speed in deep learning-based brain tumor recognition remains challenging. To address these issues, we propose a Dual-Branch Ensemble and Gated Global-Local Attention network based on EfficientNetV2S and ConvNeXt (GGLA-NeXtE2NET) to improve identification accuracy and model interpretability. For inter-class and intra-class problems, we designed a Gated Global-Local Attention (GGLA) mechanism that captures dependency information of query points in both horizontal and vertical directions, thereby obtaining global information indirectly. Simultaneously, local information is captured through multiple convolutions with a gating layer. The gating mechanism within the GGLA dynamically balances the contributions of global and local information, enabling the model to adaptively focus on the most relevant features for accurate classification. Furthermore, we introduce a dual-branch ensemble network to address the issue of image variety. This network uses two branches to extract image features at different resolutions for fusion, thereby expanding the network receptive field. Additionally, we utilized an Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) to generate images that balance MRI data and implemented multiple preprocessing techniques to tackle inherent noise in MRI images. These techniques enhance the clarity of MRI images while preserving essential details. This results in a clear improvement in the identification of tumor boundaries, crucial for accurate surgical planning and treatment strategies. We evaluated GGLA-NeXtE2NET on 3-class and 4-class brain tumor datasets and achieved 99.06%, and 99.62% overall accuracy on both datasets respectively. |
| format | Article |
| id | doaj-art-d6984a2268d04dd2a16e432ce669d120 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d6984a2268d04dd2a16e432ce669d1202025-01-15T00:02:43ZengIEEEIEEE Access2169-35362025-01-01137234725710.1109/ACCESS.2025.352551810820354GGLA-NeXtE2NET: A Dual-Branch Ensemble Network With Gated Global-Local Attention for Enhanced Brain Tumor RecognitionAdnan Saeed0https://orcid.org/0009-0001-7018-7646Khurram Shehzad1https://orcid.org/0000-0002-7129-5876Shahzad Sarwar Bhatti2Saim Ahmed3https://orcid.org/0000-0002-2302-705XAhmad Taher Azar4Department of Computer Science and Information Technology, Lahore Leads University, Lahore, PakistanFG Public School Boys, Multan Cantt., Multan, Punjab, PakistanFaculty of Computing and Emerging Technologies, Emerson University Multan, Multan, PakistanCollege of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi ArabiaCollege of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi ArabiaDue to the limited availability of training data, the diverse shapes of brain tumors among different patients, inter-class similarity, and intra-class variation, achieving high recognition accuracy and speed in deep learning-based brain tumor recognition remains challenging. To address these issues, we propose a Dual-Branch Ensemble and Gated Global-Local Attention network based on EfficientNetV2S and ConvNeXt (GGLA-NeXtE2NET) to improve identification accuracy and model interpretability. For inter-class and intra-class problems, we designed a Gated Global-Local Attention (GGLA) mechanism that captures dependency information of query points in both horizontal and vertical directions, thereby obtaining global information indirectly. Simultaneously, local information is captured through multiple convolutions with a gating layer. The gating mechanism within the GGLA dynamically balances the contributions of global and local information, enabling the model to adaptively focus on the most relevant features for accurate classification. Furthermore, we introduce a dual-branch ensemble network to address the issue of image variety. This network uses two branches to extract image features at different resolutions for fusion, thereby expanding the network receptive field. Additionally, we utilized an Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) to generate images that balance MRI data and implemented multiple preprocessing techniques to tackle inherent noise in MRI images. These techniques enhance the clarity of MRI images while preserving essential details. This results in a clear improvement in the identification of tumor boundaries, crucial for accurate surgical planning and treatment strategies. We evaluated GGLA-NeXtE2NET on 3-class and 4-class brain tumor datasets and achieved 99.06%, and 99.62% overall accuracy on both datasets respectively.https://ieeexplore.ieee.org/document/10820354/MRI brain tumordeep learningimage processingmedical image analysisattention mechanism |
| spellingShingle | Adnan Saeed Khurram Shehzad Shahzad Sarwar Bhatti Saim Ahmed Ahmad Taher Azar GGLA-NeXtE2NET: A Dual-Branch Ensemble Network With Gated Global-Local Attention for Enhanced Brain Tumor Recognition IEEE Access MRI brain tumor deep learning image processing medical image analysis attention mechanism |
| title | GGLA-NeXtE2NET: A Dual-Branch Ensemble Network With Gated Global-Local Attention for Enhanced Brain Tumor Recognition |
| title_full | GGLA-NeXtE2NET: A Dual-Branch Ensemble Network With Gated Global-Local Attention for Enhanced Brain Tumor Recognition |
| title_fullStr | GGLA-NeXtE2NET: A Dual-Branch Ensemble Network With Gated Global-Local Attention for Enhanced Brain Tumor Recognition |
| title_full_unstemmed | GGLA-NeXtE2NET: A Dual-Branch Ensemble Network With Gated Global-Local Attention for Enhanced Brain Tumor Recognition |
| title_short | GGLA-NeXtE2NET: A Dual-Branch Ensemble Network With Gated Global-Local Attention for Enhanced Brain Tumor Recognition |
| title_sort | ggla nexte2net a dual branch ensemble network with gated global local attention for enhanced brain tumor recognition |
| topic | MRI brain tumor deep learning image processing medical image analysis attention mechanism |
| url | https://ieeexplore.ieee.org/document/10820354/ |
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