The early detection of pancreatic neoplasm is a major clinical dilemma, and it is predominantly so because tumors are likely to occur with minimal contrast margins and a large spread anatomy-wide variation amongst patients on a CT scan. These complexities require to be addressed with an effective and scalable system that can assist in enhancing the salience of the subtle visual cues and provide a high level of the generalization on the multimodal imaging data. A Scalable Residual Feature Aggregation (SRFA) framework is proposed to be used to meet these conditions in this study. The framework integrates a pipeline of preprocessing followed by the segmentation using the MAGRes-UNet that is effective in making the pancreatic structures and isolating regions of interest more visible. DenseNet-121 performed with residual feature storage is used to extract features to allow deep hierarchical features to be aggregated without properties loss. To go further, hybrid HHO-BA metaheuristic feature selection strategy is used, which guarantees the best feature subset refinement. To be classified, the system is trained based on a new hybrid model that integrates the ability to pay attention on the world, which is the Vision Transformer (ViT) with the high representational efficiency of EfficientNet-B3. A dual optimization mechanism incorporating SSA and GWO is used to fine-tune hyperparameters to enhance greater robustness and less overfitting. Experimental results support the significant improvement in performance, with the suggested model reaching 96.23% accuracy, 95.58% F1-score and 94.83% specificity, the model is significantly better than the traditional CNNs and contemporary transformer-based models. Such results highlight the possibility of the SRFA framework as a useful instrument in the early detection of pancreatic tumors.

翻译：胰腺肿瘤的早期检测是一个重大的临床难题，这主要是因为在CT扫描中，肿瘤往往呈现微弱的对比度边缘，且患者间存在广泛的解剖结构变异。这些复杂性需要一个有效且可扩展的系统来解决，该系统能够增强细微视觉线索的显著性，并在多模态成像数据上实现高水平的泛化能力。本研究提出了一种可扩展残差特征聚合（SRFA）框架来满足这些要求。该框架集成了预处理流程，随后使用MAGRes-UNet进行分割，该网络能有效增强胰腺结构的可见性并分离出感兴趣区域。采用具有残差特征存储的DenseNet-121进行特征提取，以实现深度层次特征的聚合而不损失特性。进一步地，采用混合HHO-BA元启发式特征选择策略，确保最优特征子集的提炼。在分类阶段，系统基于一种新型混合模型进行训练，该模型融合了Vision Transformer（ViT）的全局注意力机制与EfficientNet-B3的高表征效率。通过结合SSA和GWO的双重优化机制对超参数进行微调，以增强模型的鲁棒性并减少过拟合。实验结果证实了性能的显著提升，所提模型达到了96.23%的准确率、95.58%的F1分数和94.83%的特异性，显著优于传统CNN及当前基于Transformer的模型。这些结果凸显了SRFA框架作为早期胰腺肿瘤检测实用工具的潜力。