A novel hybrid convolutional and transformer network for lymphoma classification.
Authors
Affiliations (7)
Affiliations (7)
- Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia. [email protected].
- Department of Computer Science and Engineering, R.M.K. College of Engineering and Technology, Puduvoyal, 601206, Tamil Nadu, India.
- Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia.
- Department of Biotechnology, P.S.R. Engineering College, Sivakasi, 626140, India.
- Department of Computer Science and Engineering, Government College of Engineering, Tirunelveli, 627007, Tamil Nadu, India.
- College of Computer and Information Sciences, Imam Muhammad Ibn Saud Islamic University (IMSIU), Riyadh, 11673, Saudi Arabia.
- Ministry of Interior, Medical Services, Riyadh, 13242, Saudi Arabia.
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.