Histopathological-based brain tumor grading using 2D-3D multi-modal CNN-transformer combined with stacking classifiers
Abstract Reliability in diagnosing and treating brain tumors depends on the accurate grading of histopathological images. However, limited scalability, adaptability, and interpretability challenge current methods for frequently grading brain tumors to accurately capture complex spatial relationships...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-11754-9 |
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| author | Naira Elazab Fahmi Khalifa Wael Gab Allah Mohammed Elmogy |
| author_facet | Naira Elazab Fahmi Khalifa Wael Gab Allah Mohammed Elmogy |
| author_sort | Naira Elazab |
| collection | DOAJ |
| description | Abstract Reliability in diagnosing and treating brain tumors depends on the accurate grading of histopathological images. However, limited scalability, adaptability, and interpretability challenge current methods for frequently grading brain tumors to accurately capture complex spatial relationships in histopathological images. This highlights the need for new approaches to overcome these shortcomings. This paper proposes a comprehensive hybrid learning architecture for brain tumor grading. Our pipeline uses complementary feature extraction techniques to capture domain-specific knowledge related to brain tumor morphology, such as texture and intensity patterns. An efficient method of learning hierarchical patterns within the tissue is the 2D-3D hybrid convolution neural network (CNN), which extracts contextual and spatial features. A vision transformer (ViT) additionally learns global relationships between image regions by concentrating on high-level semantic representations from image patches. Finally, a stacking ensemble machine learning classifier is fed concatenated features, allowing it to take advantage of the individual model’s strengths and possibly enhance generalization. Our model’s performance is evaluated using two publicly accessible datasets: TCGA and DeepHisto. Extensive experiments with ablation studies and cross-dataset evaluation validate the model’s effectiveness, demonstrating significant gains in accuracy, precision, and specificity using cross-validation scenarios. In total, our brain tumor grading model outperforms existing methods, achieving an average accuracy, precision, and specificity of 97.1%, 97.1%, and 97.0%, respectively, on the TCGA dataset, and 95%, 94%, and 95% on DeepHisto dataset. Reported results demonstrate how the suggested architecture, which blends deep learning (DL) with domain expertise, can achieve reliable and accurate brain tumor grading. |
| format | Article |
| id | doaj-art-80ac210ee2ee4dfc8244e9e3a91ffef6 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-80ac210ee2ee4dfc8244e9e3a91ffef62025-08-20T04:02:55ZengNature PortfolioScientific Reports2045-23222025-07-0115112610.1038/s41598-025-11754-9Histopathological-based brain tumor grading using 2D-3D multi-modal CNN-transformer combined with stacking classifiersNaira Elazab0Fahmi Khalifa1Wael Gab Allah2Mohammed Elmogy3Information Technology Department, Faculty of Computers and Information, Mansoura UniversityElectronics and Communications Engineering, Faculty of Engineering, Mansoura UniversityInformation Technology Department, Faculty of Computers and Information, Mansoura UniversityInformation Technology Department, Faculty of Computers and Information, Mansoura UniversityAbstract Reliability in diagnosing and treating brain tumors depends on the accurate grading of histopathological images. However, limited scalability, adaptability, and interpretability challenge current methods for frequently grading brain tumors to accurately capture complex spatial relationships in histopathological images. This highlights the need for new approaches to overcome these shortcomings. This paper proposes a comprehensive hybrid learning architecture for brain tumor grading. Our pipeline uses complementary feature extraction techniques to capture domain-specific knowledge related to brain tumor morphology, such as texture and intensity patterns. An efficient method of learning hierarchical patterns within the tissue is the 2D-3D hybrid convolution neural network (CNN), which extracts contextual and spatial features. A vision transformer (ViT) additionally learns global relationships between image regions by concentrating on high-level semantic representations from image patches. Finally, a stacking ensemble machine learning classifier is fed concatenated features, allowing it to take advantage of the individual model’s strengths and possibly enhance generalization. Our model’s performance is evaluated using two publicly accessible datasets: TCGA and DeepHisto. Extensive experiments with ablation studies and cross-dataset evaluation validate the model’s effectiveness, demonstrating significant gains in accuracy, precision, and specificity using cross-validation scenarios. In total, our brain tumor grading model outperforms existing methods, achieving an average accuracy, precision, and specificity of 97.1%, 97.1%, and 97.0%, respectively, on the TCGA dataset, and 95%, 94%, and 95% on DeepHisto dataset. Reported results demonstrate how the suggested architecture, which blends deep learning (DL) with domain expertise, can achieve reliable and accurate brain tumor grading.https://doi.org/10.1038/s41598-025-11754-9Brain tumor gradingHistopathological image analysisHybrid deep learning architectureVision transformer2D-3D convolutional neural networkStacking classifiers |
| spellingShingle | Naira Elazab Fahmi Khalifa Wael Gab Allah Mohammed Elmogy Histopathological-based brain tumor grading using 2D-3D multi-modal CNN-transformer combined with stacking classifiers Scientific Reports Brain tumor grading Histopathological image analysis Hybrid deep learning architecture Vision transformer 2D-3D convolutional neural network Stacking classifiers |
| title | Histopathological-based brain tumor grading using 2D-3D multi-modal CNN-transformer combined with stacking classifiers |
| title_full | Histopathological-based brain tumor grading using 2D-3D multi-modal CNN-transformer combined with stacking classifiers |
| title_fullStr | Histopathological-based brain tumor grading using 2D-3D multi-modal CNN-transformer combined with stacking classifiers |
| title_full_unstemmed | Histopathological-based brain tumor grading using 2D-3D multi-modal CNN-transformer combined with stacking classifiers |
| title_short | Histopathological-based brain tumor grading using 2D-3D multi-modal CNN-transformer combined with stacking classifiers |
| title_sort | histopathological based brain tumor grading using 2d 3d multi modal cnn transformer combined with stacking classifiers |
| topic | Brain tumor grading Histopathological image analysis Hybrid deep learning architecture Vision transformer 2D-3D convolutional neural network Stacking classifiers |
| url | https://doi.org/10.1038/s41598-025-11754-9 |
| work_keys_str_mv | AT nairaelazab histopathologicalbasedbraintumorgradingusing2d3dmultimodalcnntransformercombinedwithstackingclassifiers AT fahmikhalifa histopathologicalbasedbraintumorgradingusing2d3dmultimodalcnntransformercombinedwithstackingclassifiers AT waelgaballah histopathologicalbasedbraintumorgradingusing2d3dmultimodalcnntransformercombinedwithstackingclassifiers AT mohammedelmogy histopathologicalbasedbraintumorgradingusing2d3dmultimodalcnntransformercombinedwithstackingclassifiers |