Scalable AI Framework for Early Pancreatic Cancer Detection
Published:Dec 29, 2025 16:51
•1 min read
•ArXiv
Analysis
This paper proposes a novel AI framework (SRFA) for early pancreatic cancer detection using multimodal CT imaging. The framework addresses the challenges of subtle visual cues and patient-specific anatomical variations. The use of MAGRes-UNet for segmentation, DenseNet-121 for feature extraction, a hybrid metaheuristic (HHO-BA) for feature selection, and a hybrid ViT-EfficientNet-B3 model for classification, along with dual optimization (SSA and GWO), are key contributions. The high accuracy, F1-score, and specificity reported suggest the framework's potential for improving early detection and clinical outcomes.
Key Takeaways
- •Proposes a novel SRFA framework for early pancreatic cancer detection.
- •Employs a multi-stage approach including segmentation, feature extraction, feature selection, and classification.
- •Achieves high accuracy and F1-score, outperforming traditional and contemporary models.
- •Utilizes hybrid metaheuristics and dual optimization for improved performance.
Reference
“The model reaching 96.23% accuracy, 95.58% F1-score and 94.83% specificity.”