A novel hybrid convolutional and transformer network for lymphoma classification
Abstract Lymphoma poses a critical health challenge worldwide, demanding computer aided solutions towards diagnosis, treatment, and research to significantly enhance patient outcomes and combat this pervasive disease. Accurate classification of lymphoma subtypes from Whole Slide Images (WSIs) remain...
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| Format: | Article |
| Language: | English |
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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/s41598-025-11277-3 |
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| author | Mohamed Yacin Sikkandar Sankar Ganesh Sundaram Muteb Nasser Almeshari S. Sabarunisha Begum E. Siva Sankari Yousef A. Alduraywish Waeal J. Obidallah Fahad Mansour Alotaibi |
| author_facet | Mohamed Yacin Sikkandar Sankar Ganesh Sundaram Muteb Nasser Almeshari S. Sabarunisha Begum E. Siva Sankari Yousef A. Alduraywish Waeal J. Obidallah Fahad Mansour Alotaibi |
| author_sort | Mohamed Yacin Sikkandar |
| collection | DOAJ |
| description | Abstract Lymphoma poses a critical health challenge worldwide, demanding computer aided solutions towards diagnosis, treatment, and research to significantly enhance patient outcomes and combat this pervasive disease. Accurate classification of lymphoma subtypes from Whole Slide Images (WSIs) remains a complex challenge due to morphological similarities among subtypes and the limitations of models that fail to jointly capture local and global features. Traditional diagnostic methods, limited by subjectivity and inconsistencies, highlight the need for advanced, Artificial Intelligence (AI)-driven solutions. This study proposes a hybrid deep learning framework—Hybrid Convolutional and Transformer Network for Lymphoma Classification (HCTN-LC)—designed to enhance the precision and interpretability of lymphoma subtype classification. The model employs a dual-pathway architecture that combines a lightweight SqueezeNet for local feature extraction with a Vision Transformer (ViT) for capturing global context. A Feature Fusion and Enhancement Module (FFEM) is introduced to dynamically integrate features from both pathways. The model is trained and evaluated on a large WSI dataset encompassing three lymphoma subtypes: CLL, FL, and MCL. HCTN-LC achieves superior performance with an overall accuracy of 99.87%, sensitivity of 99.87%, specificity of 99.93%, and AUC of 0.9991, outperforming several recent hybrid models. Grad-CAM visualizations confirm the model’s focus on diagnostically relevant regions. The proposed HCTN-LC demonstrates strong potential for real-time and low-resource clinical deployment, offering a robust and interpretable AI tool for hematopathological diagnosis. |
| format | Article |
| id | doaj-art-5bcf897baf7d497fb921752be7f7fc15 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-5bcf897baf7d497fb921752be7f7fc152025-08-20T04:02:45ZengNature PortfolioScientific Reports2045-23222025-07-0115112010.1038/s41598-025-11277-3A novel hybrid convolutional and transformer network for lymphoma classificationMohamed Yacin Sikkandar0Sankar Ganesh Sundaram1Muteb Nasser Almeshari2S. Sabarunisha Begum3E. Siva Sankari4Yousef A. Alduraywish5Waeal J. Obidallah6Fahad Mansour Alotaibi7Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah UniversityDepartment of Computer Science and Engineering, R.M.K. College of Engineering and TechnologyDepartment of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah UniversityDepartment of Biotechnology, P.S.R. Engineering CollegeDepartment of Computer Science and Engineering, Government College of EngineeringCollege of Computer and Information Sciences, Imam Muhammad Ibn Saud Islamic University (IMSIU)College of Computer and Information Sciences, Imam Muhammad Ibn Saud Islamic University (IMSIU)Ministry of Interior, Medical ServicesAbstract Lymphoma poses a critical health challenge worldwide, demanding computer aided solutions towards diagnosis, treatment, and research to significantly enhance patient outcomes and combat this pervasive disease. Accurate classification of lymphoma subtypes from Whole Slide Images (WSIs) remains a complex challenge due to morphological similarities among subtypes and the limitations of models that fail to jointly capture local and global features. Traditional diagnostic methods, limited by subjectivity and inconsistencies, highlight the need for advanced, Artificial Intelligence (AI)-driven solutions. This study proposes a hybrid deep learning framework—Hybrid Convolutional and Transformer Network for Lymphoma Classification (HCTN-LC)—designed to enhance the precision and interpretability of lymphoma subtype classification. The model employs a dual-pathway architecture that combines a lightweight SqueezeNet for local feature extraction with a Vision Transformer (ViT) for capturing global context. A Feature Fusion and Enhancement Module (FFEM) is introduced to dynamically integrate features from both pathways. The model is trained and evaluated on a large WSI dataset encompassing three lymphoma subtypes: CLL, FL, and MCL. HCTN-LC achieves superior performance with an overall accuracy of 99.87%, sensitivity of 99.87%, specificity of 99.93%, and AUC of 0.9991, outperforming several recent hybrid models. Grad-CAM visualizations confirm the model’s focus on diagnostically relevant regions. The proposed HCTN-LC demonstrates strong potential for real-time and low-resource clinical deployment, offering a robust and interpretable AI tool for hematopathological diagnosis.https://doi.org/10.1038/s41598-025-11277-3Convolutional neural networkDeep learningLymphoma classificationMedical imagingVision transformerWhole slide images |
| spellingShingle | Mohamed Yacin Sikkandar Sankar Ganesh Sundaram Muteb Nasser Almeshari S. Sabarunisha Begum E. Siva Sankari Yousef A. Alduraywish Waeal J. Obidallah Fahad Mansour Alotaibi A novel hybrid convolutional and transformer network for lymphoma classification Scientific Reports Convolutional neural network Deep learning Lymphoma classification Medical imaging Vision transformer Whole slide images |
| title | A novel hybrid convolutional and transformer network for lymphoma classification |
| title_full | A novel hybrid convolutional and transformer network for lymphoma classification |
| title_fullStr | A novel hybrid convolutional and transformer network for lymphoma classification |
| title_full_unstemmed | A novel hybrid convolutional and transformer network for lymphoma classification |
| title_short | A novel hybrid convolutional and transformer network for lymphoma classification |
| title_sort | novel hybrid convolutional and transformer network for lymphoma classification |
| topic | Convolutional neural network Deep learning Lymphoma classification Medical imaging Vision transformer Whole slide images |
| url | https://doi.org/10.1038/s41598-025-11277-3 |
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