{"ID":2824422,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.23597","arxiv_id":"2512.23597","title":"Scalable Residual Feature Aggregation Framework with Hybrid Metaheuristic Optimization for Robust Early Pancreatic Neoplasm Detection in Multimodal CT Imaging","abstract":"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.","short_abstract":"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 th...","url_abs":"https://arxiv.org/abs/2512.23597","url_pdf":"https://arxiv.org/pdf/2512.23597v2","authors":"[\"Janani Annur Thiruvengadam\",\"Kiran Mayee Nabigaru\",\"Anusha Kovi\"]","published":"2025-12-29T16:51:13Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.IR\"]","methods":"[\"Vision Transformer\",\"Transformer\",\"Convolutional Neural Network\"]","has_code":false}