Two-Stage Transfer Learning for Slice-Level Pancreatic CT Classification: Performance Benchmarking, Confidence-Interval Reporting, Saliency Audit and a Data-Leakage Risk Analysis

2026-10-30T10:10:14Z https://lifeofresearch.com/oai2

oai:lifeofresearch.com:159 2026-10-30T06:48:35Z type:article openaire

https://lifeofresearch.com/articles/159 vemulawada Navyasri Two-Stage Transfer Learning for Slice-Level Pancreatic CT Classification: Performance Benchmarking, Confidence-Interval Reporting, Saliency Audit and a Data-Leakage Risk Analysis Life of Research 2026 pancreatic cancer; computed tomography; deep learning; transfer learning; EfficientNet; Grad-CAM; data leakage; medical image classification Journal article Creative Commons Attribution 4.0 Transfer learning with partial fine-tuning separates the two classes almost perfectly on a slice-level split, and fine-tuning EfficientNet-B0 removes the baseline's specificity failure. However, the reported figures should be read as an upper bound on clinical performance until they are reproduced with patient-level partitioning and external data. We provide a reproducible leakage-aware protocol and a reporting checklist for slice-based pancreatic CT classifiers.