Sikkandar Mohamed Yacin, Sundaram Sankar Ganesh, Almeshari Muteb Nasser, Begum S Sabarunisha, Sankari E Siva, Alduraywish Yousef A, Obidallah Waeal J, Alotaibi Fahad Mansour
Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia.
Department of Computer Science and Engineering, R.M.K. College of Engineering and Technology, Puduvoyal, 601206, Tamil Nadu, India.
Sci Rep. 2025 Jul 19;15(1):26259. doi: 10.1038/s41598-025-11277-3.
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.
淋巴瘤在全球范围内构成了严峻的健康挑战，需要计算机辅助的诊断、治疗和研究解决方案，以显著改善患者预后并对抗这种普遍存在的疾病。由于亚型之间的形态相似性以及未能联合捕捉局部和全局特征的模型的局限性，从全切片图像（WSIs）中准确分类淋巴瘤亚型仍然是一项复杂的挑战。传统诊断方法受主观性和不一致性的限制，凸显了对先进的人工智能（AI）驱动解决方案的需求。本研究提出了一种混合深度学习框架——用于淋巴瘤分类的混合卷积和Transformer网络（HCTN-LC），旨在提高淋巴瘤亚型分类的精度和可解释性。该模型采用双路径架构，将用于局部特征提取的轻量级SqueezeNet与用于捕捉全局上下文的视觉Transformer（ViT）相结合。引入了特征融合与增强模块（FFEM）以动态整合来自两条路径的特征。该模型在一个包含三种淋巴瘤亚型（慢性淋巴细胞白血病（CLL）、滤泡性淋巴瘤（FL）和套细胞淋巴瘤（MCL））的大型WSI数据集上进行训练和评估。HCTN-LC取得了卓越的性能，总体准确率为99.87%，灵敏度为99.87%，特异性为99.93%，曲线下面积（AUC）为0.9991，优于最近的几种混合模型。Grad-CAM可视化证实了该模型对诊断相关区域的关注。所提出的HCTN-LC在实时和低资源临床部署方面显示出强大的潜力，为血液病理学诊断提供了一个强大且可解释的AI工具。
